Text Summarization Research Articles

Experimental study on short-text clustering using transformer-based semantic similarity measure

Sentence clustering plays a central role in various text-processing activities and has received extensive attention for measuring semantic similarity between compared sentences. However, relatively little focus has been placed on evaluating clustering performance using available similarity measures that adopt low-dimensional continuous representations. Such representations are crucial in domains like sentence clustering, where traditional word co-occurrence representations often achieve poor results when clustering semantically similar sentences that share no common words. This article presents a new implementation that incorporates a sentence similarity measure based on the notion of embedding representation for evaluating the performance of three types of text clustering methods: partitional clustering, hierarchical clustering, and fuzzy clustering, on standard textual datasets. This measure derives its semantic information from pre-training models designed to simulate human knowledge about words in natural language. The article also compares the performance of the used similarity measure by training it on two state-of-the-art pre-training models to investigate which yields better results. We argue that the superior performance of the selected clustering methods stems from their more effective use of the semantic information offered by this embedding-based similarity measure. Furthermore, we use hierarchical clustering, the best-performing method, for a text summarization task and report the results. The implementation in this article demonstrates that incorporating the sentence embedding measure leads to significantly improved performance in both text clustering and text summarization tasks.

Automatic News Summarization using Web Scraping

In an era of information overload, accessing relevant news content efficiently is crucial. However, language barriers often hinder users from accessing news articles in languages they understand. To address this challenge, we propose an automatic news summarization and translation system. This system aggregates news articles from multiple URLs, summarizes them into concise summaries, and translates the summaries into the user's preferred language. By leveraging web scraping, natural language processing, and translation integration techniques, our system aims to provide users with access to relevant news content in their preferred language, thereby overcoming language barriers and fostering global connectivity. Keywords— automatic news summarization, translation integration, web scraping, natural language processing, language barriers, information accessibility, global connectivity, user preferences, multilingual content, web-based news aggregation

Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism

Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation.

The power of graphs in medicine: Introducing BioGraphSum for effective text summarization

In biomedicine, the expansive scientific literature combined with the frequent use of abbreviations, acronyms, and symbols presents considerable challenges for text processing and summarization. The Unified Medical Language System (UMLS) has been a go-to for extracting concepts and determining correlations in these studies; hence, the BioGraphSum model introduced in this study aims to reduce this UMLS dependence. Through adoption of an innovative perspective, sentences within a piece of text are graphically conceptualized as nodes, enabling the concept of “Malatya centrality” to be leveraged. This approach focuses on pinpointing influential nodes on a graph and, by analogy, the most pertinent sentences within the text for summarization. In order to evaluate the performance of the BioGraphSum approach, a corpus was curated that consisted of 450 contemporary scientific research articles available on the PubMed database, aligned with proven research methodology. The BioGraphSum model was subjected to rigorous testing against this corpus in order to demonstrate its capabilities. Preliminary results, especially in the precision-based and f-score-based ROUGE-(1–2), ROUGE-L, and ROUGE-SU metrics reported significant improvements when compared to other existing models considered state-of-the-art in text summarization.

Exploring the Applications and Challenges of ChatGPT in Research and Academia: A Comprehensive Review

The fast advancement of artificial intelligence (AI) technology in recent years has profoundly impacted the academic and research landscape. Among these developments, OpenAI’s ChatGPT, a cutting-edge language model, has been a game-changer for natural language creation and processing. This research review thoroughly investigates the various uses and difficulties related to incorporating ChatGPT into the domains of academia and research. ChatGPT is a flexible language model that shows promise across multiple research fields, from text summarization and language translation to supporting the automation of literature reviews. The review aims to objectively evaluate ChatGPT’s impact on academic writing processes by examining how it contributes to draft creation, writing assistance, and manuscript improvement. Furthermore, the effect of the model on education is reviewed, exploring its possible contributions to personalized education, intelligent tutoring systems, and language learning help. Though the assessment highlights the need for responsible deployment, it also discusses ethical issues, potential biases, and inherent limitations in its promising uses. This research review paves the path for future innovations and informed usage of ChatGPT within academic and research contexts by combining insights into the advantages and problems of the platform. It also provides a comprehensive awareness of the existing landscape.

A Novel Deep Learning Attention Based Sequence to Sequence Model for Automatic Abstractive Text Summarization

A Novel Deep Learning Attention Based Sequence to Sequence Model for Automatic Abstractive Text Summarization

Advancing automatic text summarization: Unleashing enhanced binary multi-objective grey wolf optimization with mutation.

Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for a system capable of processing extensive textual content and delivering a concise, yet meaningful, relevant, and useful summary. Manual summarization is both expensive and time-consuming, making it impractical for humans to handle vast amounts of data. Consequently, the need for ATS systems has become evident. These systems encounter challenges such as ensuring comprehensive content coverage, determining the appropriate length of the summary, addressing redundancy, and maintaining coherence in the generated summary. Researchers are actively addressing these challenges by employing Natural Language Processing (NLP) techniques. While traditional methods exist for generating summaries, they often fall short of addressing multiple aspects simultaneously. To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. The performance of this enhanced algorithm is assessed by comparing it with state-of-the-art algorithms using the DUC2002 dataset. Experimental results demonstrate that the proposed algorithm significantly outperforms the compared approaches.

Synaptic device-based neuromorphic computing in artificial intelligence

The application of synaptic device-based neuromorphic computing in artificial intelligence is an emerging research field aimed at simulating the structure and function of the human brain and realizing high-efficiency, low-power, and adaptive intelligent computing. This paper reviews the principles, growth and challenges of neuromorphic devices based on synapses computing and its applications and perspectives in artificial intelligence fields like an image processing as well as natural language processing. The paper first introduces the basic concepts, properties and classification of synaptic devices, as well as the basic framework and algorithms of neuromorphic computing. Then, the paper analyzes the advantages and difficulties of neuromorphic computing based on synaptic devices, including the preparation, testing, modelling and integration of the devices, as well as the systems architecture, programming and optimization. Then, this paper gives examples of the applications and effects of synaptic device-based neuromorphic computing in artificial intelligence fields such as image processing and natural language processing, including image denoising, image segmentation, image recognition, text classification, text summarization, and text generation. Finally, this paper summarizes the current research status and future synaptic device-based neuromorphic computing trends. It puts forward some research directions and suggestions to promote the development and innovation in this field.

AI YOUTUBE VIDEO SUMMARY USING NLP

The "AI YouTube Video Summary using NLP" project introduces an innovative solution to the burgeoning challenge of digesting vast amounts of video content on platforms like YouTube. With the exponential growth of online video, users often face time constraints and information overload, hindering their ability to extract valuable insights efficiently. Our project addresses this issue by harnessing the capabilities of Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically generate concise summaries of YouTube videos. Through a seamless integration with the MERN stack, our system enables users to input video URLs and receive summaries in three distinct forms: short, long, and key insights. By automating the process of transcript extraction, linguistic analysis, and summarization, our system streamlines content consumption, offering users a time-saving and effective method for accessing essential information. By leveraging machine learning algorithms and linguistic analysis techniques, our system accurately identifies and distills key themes, concepts, and insights embedded within the video content. This empowers users to gain comprehensive understanding without the need for exhaustive viewing, thereby enhancing their browsing experience and knowledge acquisition. In essence, the "AI YouTube Video Summary using NLP" project represents a significant advancement in content consumption methodologies, offering a practical solution to the challenges posed by the proliferation of video content online. Through our innovative approach, we aim to revolutionize the way users engage with YouTube videos, facilitating efficient information extraction and empowering them to make the most of their online viewing experience. Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Text Summarization, Multimedia Content Analysis, Automatic Summarization.

Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.

Medical texts present significant domain-specific challenges, and manually curating these texts is a time-consuming and labor-intensive process. To address this, natural language processing (NLP) algorithms have been developed to automate text processing. In the biomedical field, various toolkits for text processing exist, which have greatly improved the efficiency of handling unstructured text. However, these existing toolkits tend to emphasize different perspectives, and none of them offer generation capabilities, leaving a significant gap in the current offerings. This study aims to describe the development and preliminary evaluation of Ascle. Ascle is tailored for biomedical researchers and clinical staff with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle provides 4 advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. We fine-tuned 32 domain-specific language models and evaluated them thoroughly on 27 established benchmarks. In addition, for the question-answering task, we developed a retrieval-augmented generation (RAG) framework for large language models that incorporated a medical knowledge graph with ranking techniques to enhance the reliability of generated answers. Additionally, we conducted a physician validation to assess the quality of generated content beyond automated metrics. The fine-tuned models and RAG framework consistently enhanced text generation tasks. For example, the fine-tuned models improved the machine translation task by 20.27 in terms of BLEU score. In the question-answering task, the RAG framework raised the ROUGE-L score by 18% over the vanilla models. Physician validation of generated answers showed high scores for readability (4.95/5) and relevancy (4.43/5), with a lower score for accuracy (3.90/5) and completeness (3.31/5). This study introduces the development and evaluation of Ascle, a user-friendly NLP toolkit designed for medical text generation. All code is publicly available through the Ascle GitHub repository. All fine-tuned language models can be accessed through Hugging Face.

Mongolian Automatic Text Summarization Method Based on Pre-trained Model and Improved TextRank

Mongolian Automatic Text Summarization Method Based on Pre-trained Model and Improved TextRank

Crisis2Sum: An Exploratory Study on Disaster Summarization from Multiple Streams

Automatic summarization of natural and human-made disaster events is an important area to increase situational awareness for human response organizations and disaster management. However, the incorporation of multiple data sources poses a challenge to current summarization systems, as the typically large document collections exceed the input limits of neural models. Additionally, Large Language Models (LLM) often omit key information present at different positions in long context inputs. Furthermore, disaster reporting requires fine-grained information content and therefore relaxes the restriction to high compression rates, resulting into rather long summaries. In this work, we study different extractive and LLM-based abstractive baselines and highlight shortcomings in present approaches. Our experimental results on the CrisisFACTS datasets show that LLM-based approaches tend to fail in generating long informative summaries. Taking these limitations into account, we propose a disaster summarization framework and introduce query-focused extensions, which demonstrate advantages and superior performance over the baseline methods.

Revolutionizing Content Digestion: Unleashing the Power of Bidirectional and Auto-Regressive Transformers in AI-Powered Automatic Text Summarization

Revolutionizing Content Digestion: Unleashing the Power of Bidirectional and Auto-Regressive Transformers in AI-Powered Automatic Text Summarization

Identifying specific details from text to populate databases and generate summaries using Named Entity Recognition

Named Entity Recognition (NER) is a natural language processing (NLP) technique that focuses on identifying and classifying entities within a text. Entities are typically real-world objects that have names, such as people, organizations, locations, dates, percentages, currencies, and more. NER is used to extract structured information from unstructured text data and is an important component of various NLP applications, including question generation, information retrieval, text summarization, and more. Index Terms— Named Entity Recognition (NER), Natural language processing (NLP), text summarization.

Sentence salience contrastive learning for abstractive text summarization

Abstractive Text summarization aims to generate a short summary for a document while preserving salient information. Recently, contrastive learning has been extended from visual representation to summarization tasks. At present, the methods of contrastive learning summarization focus on modeling the global semantics of source documents, targets and candidate summaries to maximize their similarities. However, they ignore the influence of sentence semantics in the document. In this paper, we propose a sentence-level salience contrastive learning method to help the model capture salient information and denoise. The model expresses the sentence salience according to the semantic similarity between the summaries and sentences of the source document, and integrates the similarity distance into the contrastive loss in the form of soft weights. Therefore, our model maximize the similarity between summaries and salient information, while minimizing the similarity between summaries and potential noise. We have verified our method in three widely used datasets, CNN/Daily Mail, XSum and PubMed. The experimental results show that the proposed method can significantly improve the baseline performance and achieve competitive performance in the existing contrastive learning methods.

Text summarization with ChatGPT for drug labeling documents

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety.

Multi-Document Summarization using Discrete Bat Optimization

With the World Wide Web, we now have a wide range of data that was previously unavailable. Therefore, it has become a complex problem to find useful information in large datasets. In recent years, text summarization has emerged as a viable option for mining relevant data from massive collections of texts. We may classify summarizing as either "single document" or "multi document" depending on how many source documents we are working with. Finding an accurate summary from a collection of documents is more difficult for researchers than doing it from a single document. For this reason, this research proposes a Discrete Bat algorithm Optimization based multi document summarizer (DBAT-MDS) to tackle the issue of multi document summarizing. Comparisons are made between the proposed DBAT-MDS based model and three different summarization algorithms that take their inspiration from the natural world. All methods are evaluated in relation to the benchmark Document Understanding Conference (DUC) datasets using a variety of criteria, such as the ROUGE score and the F score. Compared to the other summarizers used in the experiment, the suggested method performs much better.

Supervised weight learning-based PSO framework for single document extractive summarization

The need for automatic text summarization is natural: there is a huge volume of information available online, which prompts for a widespread interest in extracting relevant information in a concise and understandable manner. Here, automated text summarization has been treated as an extractive single-document summarization problem in the proposed system. To solve this problem, a particle swarm optimisation (PSO) algorithm-based approach is suggested, with the goal of producing good summaries in terms of content coverage, informativeness, and readability.This paper introduces XSumm-PSO: a new approach based on PSO optimization technique in a supervised manner for extractive summarization. Further, this paper also contributes a new feature “incorrect word” that captures misspelled words in the candidate sentences. This feature is combined with nine existing features used by proposed model to generate error free summaries. As a result, the proposed XSumm-PSO framework produces superior performance achieving improvements of +2.7%, +0.8%, and +0.8% for ROUGE-1, ROUGE-2, and ROUGE-L scores, respectively, on DUC 2002 dataset, over state-of-the-art techniques. The corresponding improvements on the CNN/DailyMail dataset are +0.97%, +0.25%, and +0.49%.We also performed sample t-test, showing the proposed approach is statistically consistent across various runs.

Comparative Study on Automated Reference Summary Generation using BERT Models and ROUGE Score Assessment

Automatic text summarization is a sub-area in text mining in which a computer system determines the most informative information in the original text to produce a summary for certain jobs and users. In the development of the systems, one of the most important tasks is to evaluate the quality of summaries produced by the systems. Generally, the evaluation task becomes laborious, time-consuming, and expensive because it requires significant efforts on annotation tasks for humans to manually create reference summaries. Being able to generate automatic reference summaries would promote the development of summarization systems in term of speed and evaluation. In this paper, we proposed an Auto-Ref Summary Generation framework for automatically generating reference summaries used in the generic text summarization evaluation task, the Sliced Summary. Given a set of clusters from a cluster ground-truth label dataset, variants of BERT models were utilized for creating cluster representations. The automatic reference summaries were later generated through a centroid-based summarization approach. Overall, DistilBERT, ROBERTa, and SBERT have played crucial roles in automatic summary generation, achieving the highest ROUGE-1 score of 0.47060. However, this does not meet our expectation on text coherence and readability aspects. Although the summaries generated through our proposed framework could not be used as the replacement of the manual summaries, this study has shed new light on the acquisition of automatic reference summaries from a ground-truth label dataset.

A study of extractive summarization of long documents incorporating local topic and hierarchical information

In recent years, the transformer-based language models have achieved remarkable success in the field of extractive text summarization. However, there are still some limitations in this kind of research. First, the transformer language model usually regards the text as a linear sequence, ignoring the inherent hierarchical structure information of the text. Second, for long text data, traditional extractive models often focus on global topic information, which poses challenges in how they capturing and integrating local contextual information within topic segments. To address these issues, we propose a long text extractive summarization model that employs a local topic information extraction module and a text hierarchical extraction module to capture the local topic information and document's hierarchical structure information of the original text. Our approach enhances the ability to determine whether a sentence belongs to the summary. In this experiment, ROUGE score is used as the experimental evaluation index, and evaluates the model on three large public datasets. Through experimental validation, the model demonstrates superior performance in terms of ROUGE-1, ROUGE-2, and ROUGE-L scores compared to current mainstream summarization models, affirming the effectiveness of incorporating local topic information and document hierarchical structure into the model.