Summary Generation Research Articles

Automatic summarization of cooking videos using transfer learning and transformer-based models

The proliferation of cooking videos on the internet these days necessitates the conversion of these lengthy video contents into concise text recipes. Many online platforms now have a large number of cooking videos, in which, there is a challenge for viewers to extract comprehensive recipes from lengthy visual content. Effective summary is necessary in order to translate the abundance of culinary knowledge found in videos into text recipes that are easy to read and follow. This will make the cooking process easier for individuals who are searching for precise step by step cooking instructions. Such a system satisfies the needs of a broad spectrum of learners while also improving accessibility and user simplicity. As there is a growing need for easy-to-follow recipes made from cooking videos, researchers are looking on the process of automated summarization using advanced techniques. One such approach is presented in our work, which combines simple image-based models, audio processing, and GPT-based models to create a system that makes it easier to turn long culinary videos into in-depth recipe texts. A systematic workflow is adopted in order to achieve the objective. Initially, Focus is given for frame summary generation which employs a combination of two convolutional neural networks and a GPT-based model. A pre-trained CNN model called Inception-V3 is fine-tuned with food image dataset for dish recognition and another custom-made CNN is built with ingredient images for ingredient recognition. Then a GPT based model is used to combine the results produced by the two CNN models which will give us the frame summary in the desired format. Subsequently, Audio summary generation is tackled by performing Speech-to-text functionality in python. A GPT-based model is then used to generate a summary of the resulting textual representation of audio in our desired format. Finally, to refine the summaries obtained from visual and auditory content, Another GPT-based model is used which combines the output of the frame summary and audio summary modules and give the final enhanced summary. By minimizing the complications involved with traditional and sophisticated methodologies, this research helps with the development of a straightforward but efficient cooking video summarization system. The results achieved in the work are on par with the existing work in the respective field which demonstrates comparable performance and efficacy in converting cooking videos into detailed recipe texts.

A Knowledge Graph Summarization Model Integrating Attention Alignment and Momentum Distillation

The integrated knowledge graph summarization model improves summary performance by combining text features and entity features. However, the model still has the following shortcomings: the knowledge graph data used introduce data noise that deviates from the original text semantics; and the text and knowledge graph entity features cannot be fully integrated. To address these issues, a knowledge graph summarization model integrating attention alignment and momentum distillation (KGS-AAMD) is proposed. The pseudo-targets generated by the momentum distillation model serve as additional supervision signals during training to overcome data noise. The attention-based alignment method lays the foundation for the subsequent full integration of text and entity features by aligning them. Experimental results on two public datasets, namely CNN / Daily Mail and XSum, show that KGS-AAMD surpasses multiple baseline models and ChatGPT in terms of the quality of summary generation, exhibiting significant performance advantages.

Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study.

The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging. This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media. We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource settings, with GPT-4. For this proof-of-concept study, we used user-generated data from Reddit to answer clinicians' questions on the use of xylazine and ketamine. Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO, evaluated for 20 queries with 76 samples. There was no statistically significant difference between GPT-4 and Nous-Hermes-2-7B-DPO for coverage (Mann-Whitney U=733.0; n1=37; n2=39; P=.89 two-tailed), coherence (U=670.0; n1=37; n2=39; P=.49 two-tailed), relevance (U=662.0; n1=37; n2=39; P=.15 two-tailed), length (U=672.0; n1=37; n2=39; P=.55 two-tailed), and hallucination (U=859.0; n1=37; n2=39; P=.01 two-tailed). A statistically significant difference was noted for the Coleman-Liau Index (U=307.5; n1=20; n2=16; P<.001 two-tailed). Our RAG framework can effectively answer medical questions about targeted topics and can be deployed in resource-constrained settings.

A Sentiment Analysis and Semantic Comprehension-Integrated Automatic Abstraction Model for English Texts

Automatic abstraction has been a more prevalent technical demand in text analysis. However, due to the complex relationship between sentence structure and semantics, current automatic abstraction models in English texts still suffer from some challenges. Therefore, this paper explores a new type of English text automatic abstraction model that combines sentiment analysis and semantic perception technology. First, it is supposed to make serialization and extraction of English texts, resulting in multi-level sequential text. Then, a hierarchical multihead attention mechanism is introduced to achieve dynamic word vectorization. Finally, it is input into a multi-angle convolutional network, in which each token-level vector in the sentence is input for multiangle sentiment analysis and double-layer semantic perception. Hence, the automatic abstract result for long texts can be achieved. After that, experiments are conducted on multiple English text datasets to compare the proposal with other benchmark methods. The results show that the model has achieved significant improvement in the generation of text summaries, which can more effectively generate text summaries with rich emotions and accurate semantics.

Generative artificial intelligence (AI) and its performance at indexing tasks

Artificial intelligence (AI) and its opportunities and threats are currently the subject of extensive debate. This article begins by briefly outlining the technologies comprising AI and how they are used in publishing, before reporting on experiments with generative AI chatbots Claude and Adobe AI Assistant. It evaluates their performance at indexing-related tasks such as summary and keyword generation, and at the creation of indexes. It concludes by discussing the factors that may impact on the extension of AI tools into indexing.

An Exploration of Extractive method and Abstractive Method of Text Summarization with Various Approaches, Techniques and Datasets

Abstract: In today’s era, an enormous amount of data available which is in complex form such as Social media content, Images, Audio, Video and Text data form. The mechanism is very much needed to provide these types of data in simple and easy to understandable form. In this paper, importance is given on text summarization of large amount of textual data of documents must be specified in concise and understandable form. Automatic Text Summarization is the technique which is used to represent most significant concepts in precise and comprehensible form from original or source document to target document providing greater flexibility and convenience. Since the advancement in emerging technologies leads to the challenges in research field which is leading towards to need arises to analyze and should be resolved over the time period. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. Automatic text summarization process model can be divided into three steps in the preprocessing step, source text interpretation to representation, source text representation transform to summary text representation with an algorithm. Finally, summary text generation from summary representation. In this paper discussion were made about some of the extractive based and abstractive based methods of text summarization for single documents. Various approaches of text summarization, methods needed for it along with tools and techniques, metrics needed to measure quality of generated text summary applications have been discussed in detail. This survey paper highlights the main advancement in the field of Automatic Text Summarization and provides the key methods with their benefits and limitations. Gives an overview of the past, present and future direction with certain solution to the challenges arises in text summary generation process with extractive and abstractive methods.

Unified extractive-abstractive summarization: a hybrid approach utilizing BERT and transformer models for enhanced document summarization

With the exponential proliferation of digital documents, there arises a pressing need for automated document summarization (ADS). Summarization, a compression technique, condenses a source document into concise sentences that encapsulate its salient information for summary generation. A primary challenge lies in crafting a dependable summary, contingent upon both extracted features and human-established parameters. This article introduces an intelligent methodology that seamlessly integrates extractive and abstractive techniques to ensure heightened relevance between the input document and its summary. Initially, input sentences undergo transformation into representations utilizing BERT, subsequently transposed into a symmetric matrix based on their similarity. Semantically congruent sentences are then extracted from this matrix to construct an extractive summary. The transformer model integrates an objective function highly symmetric and invariant under unitary transformation for language generation. This model refines the extracted informative sentences and generates an abstractive summary akin to manually crafted summaries. Employing this hybrid summarization technique on the CNN/DailyMail dataset and DUC2004, we evaluate its efficacy using ROUGE metrics. Results demonstrate the superiority of our proposed technique over conventional summarization methods.

Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization.

Automated summarization of legal texts poses a significant challenge due to the complex and specialized nature of legal documentation. Despite the recent progress in reinforcement learning for natural language text summarization, its application in the legal domain has been less effective. This paper introduces SAC-VAE, a novel reinforcement learning framework specifically designed for legal text summarization. We leverage a Variational Autoencoder (VAE) to condense the high-dimensional state space into a more manageable lower-dimensional feature space. These compressed features are subsequently utilized by the Soft Actor-Critic (SAC) algorithm for policy learning, facilitating the automated generation of summaries from legal texts. Through comprehensive experimentation, we have empirically demonstrated the effectiveness and superior performance of the SAC-VAE framework in legal text summarization.

HVCMM: A Hybrid-View Abstractive Model of Automatic Summary Generation for Teaching.

With the continuous development of educational informatization, more and more emerging technologies are applied in teaching activities. These technologies provide massive and multidimensional information for teaching research, but at the same time, the information obtained by teachers and students presents an explosive increase. Extracting the core content of the class record text through text summarization technology to generate concise class minutes can significantly improve the efficiency of teachers and students to obtain information. This article proposes a hybrid-view class minutes automatic generation model (HVCMM). The HVCMM model uses a multilevel encoding strategy to encode the long text of the input class records to avoid memory overflow in the calculation after the long text is input into the single-level encoder. The HVCMM model uses the method of coreference resolution and adds role vectors to solve the problem that the excessive number of participants in the class may lead to confusion about the referential logic. Machine learning algorithms are used to analyze the topic and Section of the sentence to capture structural information. We test the HVCMM model on the Chinese class minutes dataset (CCM) and the augmented multiparty interaction (AMI) dataset, and the results show that the HVCMM model outperforms other baseline models on the ROUGE metric. With the help of the HVCMM model, teachers can improve the efficiency of reflection after class and improve the teaching level. Students can review the key content to strengthen their understanding of what they have learned with the help of the class minutes automatically generated by the model.

Integration of customised LLM for discharge summary generation in real-world clinical settings: a pilot study on RUSSELL GPT

Integration of customised LLM for discharge summary generation in real-world clinical settings: a pilot study on RUSSELL GPT

Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization

Abstract Despite the remarkable performance of generative large language models (LLMs) on abstractive summarization, they face two significant challenges: their considerable size and tendency to hallucinate. Hallucinations are concerning because they erode reliability and raise safety issues. Pruning is a technique that reduces model size by removing redundant weights, enabling more efficient sparse inference. Pruned models yield downstream task performance comparable to the original, making them ideal alternatives when operating on a limited budget. However, the effect that pruning has upon hallucinations in abstractive summarization with LLMs has yet to be explored. In this paper, we provide an extensive empirical study across five summarization datasets, two state-of-the-art pruning methods, and five instruction-tuned LLMs. Surprisingly, we find that hallucinations are less prevalent from pruned LLMs than the original models. Our analysis suggests that pruned models tend to depend more on the source document for summary generation. This leads to a higher lexical overlap between the generated summary and the source document, which could be a reason for the reduction in hallucination risk.1

Unleashing the Power of Contrastive Learning for Zero-Shot Video Summarization.

Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing. Past efforts have invariantly involved training summarization models with annotated summaries or heuristic objectives. In this work, we reveal that features pre-trained on image-level tasks contain rich semantic information that can be readily leveraged to quantify frame-level importance for zero-shot video summarization. Leveraging pre-trained features and contrastive learning, we propose three metrics featuring a desirable keyframe: local dissimilarity, global consistency, and uniqueness. We show that the metrics can well-capture the diversity and representativeness of frames commonly used for the unsupervised generation of video summaries, demonstrating competitive or better performance compared to past methods when no training is needed. We further propose a contrastive learning-based pre-training strategy on unlabeled videos to enhance the quality of the proposed metrics and, thus, improve the evaluated performance on the public benchmarks TVSum and SumMe.

Teaching AI to Summarize Like a Human: AReinforcement Learning Experiment

Text summarization is a crucial task in natural language processing (NLP), aiming to distill extensive information into concise and coherent summaries. Traditional summarization methods, including both extractive and abstractive techniques, face challenges in generating summaries that balance brevity and informativeness. This paper explores the application of Reinforce- ment Learning with Human Feedback (RLHF) to address these challenges and enhance the quality of text summarization.We introduce an RLHF-based approach using the FLAN-T5-small model, which integrates human feedback into the reinforcement learning framework to refine summary generation. Our method leverages a dataset from the Hugging Face datasets library, consisting of diverse document-summary pairs. The model is pre-trained on a large corpus and fine-tuned using human feedback, which serves as a reward signal to guide the model towards generating more relevant and coherent summaries.Our experimental results demonstrate that the RLHF-enhanced model significantly outperforms traditional summarization methods. Quantitative evaluations using ROUGE and BLEU metrics reveal substantial improvements in summary quality, with increases of up to 12.5% in ROUGE- 1 and 9.8% in BLEU scores over baseline methods. Qualitative assessments by human evaluators further confirm that the RLHF-based model produces summaries that are more aligned with human expectations in terms of coherence and relevance.This study highlights the potential of RLHF to overcome the limitations of conventional summarization tech- niques, offering a robust framework for generating high-quality summaries across various domains. Future work will explore the scalability of this approach to more complex summarization tasks and the integration of additional feedback mechanisms to further enhance performance.

A new approach to video summary generation

This paper describes a video summary approach for an actual video file that provides a rapid way to assess its information. This is a difficult task to identify relevant or informative sections. When the original video demands knowledge of its content, we build a video summary via extracting in-depth characteristics of each original video. Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant information provides a concise and accurate representation of the original video clips by providing the most representative summary with increased interest through the application of a matching function between the current and prior frames. The primary goal of this work is to give a strategy for recording video clips using security cameras and converting them into a short video to provide a clear analysis of the video by reducing excess and extracting the key video contents into the real video. In a lengthy video clip, viewers may not have enough time to see the full thing. The viewer may be interested in seeing a specific topic under the label "Important". It may be composed of pieces that may be shared on social media and communicate the semantics of long videos, particularly video snippets that must be submitted to Internet. To browse, will be needed a network with adequate bandwidth. That is, non-affiliated video streaming, indexing, and retrieval of video content makes it valuable to viewers.

PROCESSAMENTO DE LINGUAGEM NATURAL PARA RECONHECIMENTO DE ENTIDADES NOMEADAS EXTRAÍDAS DE CORPUS

This article presents a code implementation to automate the reading, analysis and generation of summaries of unstructured web texts, highlighting the crucial role of Natural Language Processing (NLP). The process was divided into two stages: data collection and pre-processing. In the first stage, a scientific article was selected and relevant data was extracted via web scraping, organized into an HTML page hosted online. In pre-processing, tokenization, normalization, stopword removal and word counting were performed. Using NLTK, the most important sentences were identified and ranked based on keyword frequency, allowing the selection of the most relevant sentences from the introduction, methodology, results/dicussion and conclusion sections. Discrepancies such as links and figure descriptions have been removed to improve the clarity of the abstract. The “most_common()” method was used to select the most relevant words in each section. After additional processing to delete unnecessary words, the final summary was clear and understandable, despite minor flaws. This automated approach provides an efficient way to synthesize information from scientific articles, optimizing the textual analysis process.

Review Paper on Deep RL for Video Summarization

In recent years, the explosive growth of video content has heightened the need for efficient summarization techniques to distill lengthy videos into concise, informative summaries. Traditional approaches to video summarization often rely on heuristic-based methods or supervised learning techniques, which can be limited by their reliance on predefined features or extensive labeled datasets. To address these limitations, this paper explores the application of Deep Reinforcement Learning (DRL) for video summarization. DRL offers a dynamic framework where an agent learns to optimize summarization strategies through interaction with the video content, enabling adaptive and context-aware summarization. We propose a novel DRL-based framework that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract and represent temporal features from video frames. Actor- critic architecture is utilized, where the actor generates candidate summaries and the critic evaluates their quality based on a reward function designed to balance in formativeness and brevity. We introduce a new reward function that incorporates both content relevance and diversity to encourage the generation of summaries that effectively capture key moments and maintain narrative coherence. Experimental results on benchmark video datasets demonstrate that our DRL-based approach significantly outperforms traditional methods in terms of both summary quality and user satisfaction. The proposed method not only achieves state-of-the-art performance but also offers greater flexibility and adaptability to diverse video content. This work highlights the potential of DRL in advancing video summarization and opens avenues for future research in optimizing video content extraction and representation

Cross-Modal Retrieval-enhanced code Summarization based on joint learning for retrieval and generation

Context:Code summarization refers to a task that automatically generates a natural language description of a code snippet to facilitate code comprehension. Existing methods have achieved satisfactory results by incorporating information retrieval into generative deep-learning models for reusing summaries of existing code. However, most of these existing methods employed non-learnable generic retrieval methods for content-based retrieval, resulting in a lack of diversity in the retrieved results during training, thereby making the model over-reliant on retrieved results and reducing the generative model’s ability to generalize to unknown samples. Objective:To address this issue, this paper introduces CMR-Sum: a novel Cross-Modal Retrieval-enhanced code Summarization framework based on joint learning for generation and retrieval tasks, where both two tasks are allowed to be optimized simultaneously. Method:Specifically, we use a cross-modal retrieval module to dynamically alter retrieval results during training, which enhances the diversity of the retrieved results and maintains a relative balance between the two tasks. Furthermore, in the summary generation phase, we employ a cross-attention mechanism to generate code summaries based on the alignment between retrieved and generated summaries. We conducted experiments on three real-world datasets, comparing the performance of our method with baseline models. Additionally, we performed extensive qualitative analysis. Result:Results from qualitative and quantitative experiments indicate that our approach effectively enhances the performance of code summarization. Our method outperforms both the generation-based and the retrieval-enhanced baselines. Further ablation experiments demonstrate the effectiveness of each component of our method. Results from sensitivity analysis experiments suggest that our approach achieves good performance without requiring extensive hyper-parameter search. Conclusion:The direction of utilizing retrieval-enhanced generation tasks shows great potential. It is essential to increase the diversity of retrieval results during the training process, which is crucial for improving the generality and the performance of the model.

TASP: Topic-based abstractive summarization of Facebook text posts

Summarizing trending topics in large collections of Facebook posts is particularly relevant to profile social user activities and interests. However, automatically generating these summaries poses significant challenges due to the high heterogeneity of the input data, the limited fluency of extractive summaries, and the absence of abstractive summarization methods capable of handling multiple posts simultaneously. Existing abstractive models are either not suited to handle large post collections or disregard topic-level text relations. In this work, we present TASP, a novel tool for trending topic detection and summarization from English-written Facebook posts. It trains abstractive summarization models on multi-post collections by leveraging a shortlist of authoritative posts published by renowned newspapers. At inference time, TASP first creates clusters of semantically similar social posts, each one representing a distinct topic, using pre-trained transformer-based language models. Then, it generates abstractive summaries of the clusters for which authoritative information is missing. To the best of our knowledge, TASP is the first available tool suited to abstractive multi-post summarization. We test our approach on a large-scale dataset of real Facebook posts. The results show (1) The higher effectiveness of transformer-based approaches in generating topic-specific post clusters compared to traditional methods. (2) The importance of attending long pieces of text in multi-post abstractive summary generation.

Improving ROUGE‐1 by 6%: A novel multilingual transformer for abstractive news summarization

SummaryNatural language processing (NLP) has undergone a significant transformation, evolving from manually crafted rules to powerful deep learning techniques such as transformers. These advancements have revolutionized various domains including summarization, question answering, and more. Statistical models like hidden Markov models (HMMs) and supervised learning have played crucial roles in laying the foundation for this progress. Recent breakthroughs in transfer learning and the emergence of large‐scale models like BERT and GPT have further pushed the boundaries of NLP research. However, news summarization remains a challenging task in NLP, often resulting in factual inaccuracies or the loss of the article's essence. In this study, we propose a novel approach to news summarization utilizing a fine‐tuned Transformer architecture pre‐trained on Google's mt‐small tokenizer. Our model demonstrates significant performance improvements over previous methods on the Inshorts English News dataset, achieving a 6% enhancement in the ROUGE‐1 score and reducing training loss by 50%. This breakthrough facilitates the generation of reliable and concise news summaries, thereby enhancing information accessibility and user experience. Additionally, we conduct a comprehensive evaluation of our model's performance using popular metrics such as ROUGE scores, with our proposed model achieving ROUGE‐1: 54.6130, ROUGE‐2: 31.1543, ROUGE‐L: 50.7709, and ROUGE‐LSum: 50.7907. Furthermore, we observe a substantial reduction in training and validation losses, underscoring the effectiveness of our proposed approach.

A web-based, LLM-powered AI symptom summarization tool (ASST) for monitoring of breast cancer treatment toxicity.

e13622 Background: Traditional methods for capturing patient-reported outcomes (PROs) are often time-consuming and may lead to underreporting of symptoms by physicians. With advancements in artificial intelligence (AI), particularly large language models (LLMs) like GPT-4, there is potential to revolutionize this aspect of patient care. Methods: This study developed and tested a GPT-4-based web application for monitoring cancer treatment toxicity in breast cancer patients. The application utilized 35 items from the PRO-CTCAE scale to create an interactive form for patients to report treatment-related symptoms. Upon form completion, a natural language summary of the patient’s responses was generated using the GPT-4 API for physician review. Nine radiation oncologists (5 attendings, 4 residents) evaluated the summaries of virtual patients using the adapted Physician Documentation Quality Index (PDQI-9). PDQI-9 consists of 9 items scored using a 5-point Likert scale (1 -not at all- to 5. -extremely-). IRB approval was not required because this study did not use real patient data, in accordance with the 2018 Revised Common Rule Requirements of the NIH. All data used were researcher-generated and non-identifiable. Results: The mean time for textual summary generation was 7 (5.7-9.2) seconds. The AI-Symptom Summarization Tool (ASST) mean scores were 4.25 for accuracy and 4 for thoroughness. Usefulness, organization, comprehensibility, concision, synthesis quality and consistency were all rated between 3 and 4. Overall score was 42/50. No hallucinations (false information made up by the LLM) were found. Conclusions: The GPT-4-based application for cancer treatment toxicity monitoring demonstrates significant promise in enhancing the quality and efficiency of patient care. By automating the documentation of patient-reported symptoms, this tool could allow physicians to focus more time on patient interaction and individualized care. As AI technologies continue to evolve, their integration into clinical practice must be approached with caution, emphasizing augmentation over replacement and the importance of verification to maintain trust in these new tools.