Text Summarization Research Articles

Social-sum-Mal: A Dataset for Abstractive Text Summarization in Malayalam

Abstractive text summarization techniques for Malayalam language is still in its infancy. The lack of benchmarked datasets for this task is one of the constraints in developing and testing good models. Malayalam has seven nominal case forms, two nominal number forms, and three gender forms. It is subjected to extreme agglutination and inflection. Due to this, the translation of other text summarization datasets to Malayalam may not capture these case forms effectively. Therefore curation of datasets from scratch is highly demanded for specific text-processing applications in Malayalam. This paper introduces a novel dataset designed specifically for advancing the field of automatic abstractive text summarization in Malayalam language. The dataset is curated to address the unique linguistic characteristics of the Malayalam language. It is named as Social-sum-Mal dataset, capable of addressing three different types of summarization tasks- long, extreme, and query-based summarizations. In addition, Social-sum-Mal can be extended for other applications like text classification, multi-document summarization, and question answering. To enhance the dataset transparency, a datasheet is created for Social-sum-Mal. Data accuracy and annotator biases are evaluated using proper testing strategies including Jaccard, cosine, and overlap similarities. The correctness of the dataset is further evaluated by comparing it with a deep-learning-based text summarization model.

Evaluating the Effectiveness of the LexRank and LSA Algorithm in Automatic Text Summarization for Indonesian Language

The aim of this study is to evaluate how effective the Lexrank algorithm and Latent semantic analysis (LSA) are in automatic text summarization for the Indonesian language. This research focuses on natural language processing and handling of excessive data. We applied both algorithms to generate text summaries using the INDOSUM dataset, which contains about 20,000 news articles in Indonesian with manual summaries. To assess performance, the ROUGE metric was used, which includes aspects of precision, recall, and F1 score. In all tested metrics, LSA outperformed Lexrank. LSA had a precision of 0.57, recall of 0.67, and an F1 score of 0.59, whereas Lexrank had a precision of 0.46, recall of 0.52, and an F1 score of 0.48. These result indicate that LSA is better at gathering important information from the original text than Lexrank.

Extractive text summarization on medical insights using fine-tuned transformers

ABSTRACT Text summarization is a fundamental Natural language processing task that plays a crucial role in efficiently condensing large textual documents into concise and clear summaries for human comprehension. The amount of data being generated in the medical domain nowadays requires substantial application of the current deep learning approaches such as transformers. The main goal of this research is to extract relevant summaries from the abstracts of the research articles published related to cancer, blood cancer, tinnitus, and Alzheimer's. As the domain-related data requires special attention, our approach uses a fine-tuned transformer model, to guarantee that the summaries produced are not only brief but also accurate. Moreover, as a part of this research, we have effectively collected the information from PubMed and also prepared the data for analysis. A comparative analysis of the Bidirectional and Auto-Regressive Transformers (BART), Text-to-Text Transfer Transformer (T5), Textrank, and Lexrank models on the dataset is carried out in this study to understand the medical insights effectively. The fine-tuned transformer's performance in comparison with other models brings out a newer dimension for future studies.

Transformer-Based Video Comment Analysis

In this work, we conduct a comprehensive analysis of sentiment in Bilibili comments using a Transformer-based model. We employ the mT5_m2o_chinese_simplified_crossSum model, a multitasking Transformer model specifically designed for summarizing Chinese text. The study involves scraping comments from a video related to the "trolley problem," followed by data cleaning and preprocessing. The preprocessed data is fed into the Transformer model, which generates summaries that accurately reflect the main viewpoints and discussions in the comments. Our results show that the model effectively captures key ethical dilemmas, personal opinions, and emotional responses, providing a nuanced understanding of public sentiment. This research highlights the potential of advanced NLP techniques in processing large volumes of user-generated content, offering valuable insights for businesses and researchers in understanding public opinion and facilitating ethical discussions. The study underscores the importance of optimizing Transformer models for specific tasks, demonstrating their flexibility and performance in text summarization. These findings provide a robust foundation for future applications and innovations in natural language processing.

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.

Enhancing Knowledge Management in Space Science: Leveraging Knowledge Graphs and Text Summarization Techniques

In recent years Space exploration has evolved from the realm of governments and government agencies and has emerged as a promising industry with diverse stakeholders ranging from R&D labs, Universities and even private firms. There is also increasing international collaboration between governments in this important area with important applications in telecommunications, disaster control, not to mention defence applications. As a result there is a growing amount of data and information making it difficult to navigate through this vast amount of data and information. This paper examines the use of knowledge graphs and text summarization tools fo effective knowledge management in the domain.

Evaluation metrics on text summarization: comprehensive survey

Evaluation metrics on text summarization: comprehensive survey

Bidirectional and Auto-Regressive Transformer (BART) for Indonesian Abstractive Text Summarization

Bidirectional and Auto-Regressive Transformer (BART) for Indonesian Abstractive Text Summarization

Cross-Domain Document Summarization Model via Two-Stage Curriculum Learning

Cross-Domain Document Summarization Model via Two-Stage Curriculum Learning

An indicator-based multi-objective variable neighborhood search approach for query-focused summarization

Currently, automatic multi-document summarization is an interesting subject in numerous fields of study. As a part of it, query-focused summarization is becoming increasingly important in recent times. These methods can automatically produce a summary based on a query given by the user, including the most relevant information from the query at the same time as the redundancy among sentences is reduced. This can be achieved by developing and applying a multi-objective optimization approach. In this paper, an Indicator-based Multi-Objective Variable Neighborhood Search (IMOVNS) algorithm has been designed, implemented, and tested for the query-focused extractive multi-document summarization problem. Experiments have been carried out with datasets from Text Analysis Conference (TAC). The results were evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. IMOVNS has greatly improved the results presented in the scientific literature, providing improvement percentages in ROUGE metric reaching up to 69.24% in ROUGE-1, up to 57.70% in ROUGE-2, and up to 77.37% in ROUGE-SU4 scores. Hence, the proposed IMOVNS offers a promising solution to the query-focused summarization problem, thus highlighting its efficacy and potential for enhancing automatic summarization techniques.

FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization

Framing theory is a widely accepted theoretical framework in the field of news communication studies, frequently employed to analyze the content of news reports. This paper innovatively introduces framing theory into the text summarization task and proposes a news text summarization method based on framing theory to address the global context of rapidly increasing speed and scale of information dissemination. Traditional text summarization methods often overlook the implicit deep-level semantic content and situational frames in news texts, and the method proposed in this paper aims to fill this gap. Our deep learning-based news frame identification module can automatically identify frame elements in the text and predict the dominant frame of the text. The frame-aware summarization generation model (FrameSum) can incorporate the identified frame feature into the text representation and attention mechanism, ensuring that the generated summary focuses on the core content of the news report while maintaining high information coverage, readability, and objectivity. Through empirical studies on the standard CNN/Daily Mail dataset, we found that this method performs significantly better in improving summary quality and maintaining the accuracy of news facts.

Utilizing Large Language Models in Ophthalmology: The Current Landscape and Challenges.

A large language model (LLM) is an artificial intelligence (AI) model that uses natural language processing (NLP) to understand, interpret, and generate human-like language responses from unstructured text input. Its real-time response capabilities and eloquent dialogue enhance the interactive user experience in human-AI communication like never before. By gathering several sources on the internet, LLM chatbots can interact and respond to a wide range of queries, including problem solving, text summarization, and creating informative notes. Since ophthalmology is one of the medical fields integrating image analysis, telemedicine, AI, and other technologies, LLMs are likely to play an important role in eye care in the near future. This review summarizes the performance and potential applicability of LLMs in ophthalmology according to currently available publications.

Enhanced ICD-10 code assignment of clinical texts: A summarization-based approach

BackgroundAssigning International Classification of Diseases (ICD) codes to clinical texts is a common and crucial practice in patient classification, hospital management, and further statistics analysis. Current auto-coding methods mainly transfer this task to a multi-label classification problem. Such solutions are suffering from high-dimensional mapping space and excessive redundant information in long clinical texts. To alleviate such a situation, we introduce text summarization methods to the ICD coding regime and apply text matching to select ICD codes. MethodWe focus on the tenth revision of the ICD (ICD-10) coding and design a novel summarization-based approach (SuM) with an end-to-end strategy to efficiently assign ICD-10 code to clinical texts. In this approach, a knowledge-guided pointer network is purposed to distill and summarize key information in clinical texts precisely. Then a matching model with matching-aggregation architecture follows to align the summary result with code, tuning the one-vs-all scenario to one-vs-one matching so that the large-label-space obstacle laid in classification approaches would be avoided. ResultThe 12,788 ICD-10 coded discharge summaries from a Chinese hospital were collected to evaluate the proposed approach. Compared with existing methods, the purposed model achieves the greatest coding results with Micro AUC of 0.9548, MRR@10 of 0.7977, Precision@10 of 0.0944, and Recall@10 of 0.9439 for the TOP-50 Dataset. Results on the FULL-Dataset remain consistent. Also, the proposed knowledge encoder and applied end-to-end strategy are proven to facilitate the whole model to gain efficacy in selecting the most suitable code. ConclusionThe proposed automatic ICD-10 code assignment approach via text summarization can effectively capture critical messages in long clinical texts and improve the performance of ICD-10 coding of clinical texts.

Encoder-Decoder Transformers for Textual Summaries on Social Media Content

Social media has a leading role to our lives due to radical upgrade of internet and smart technology. It is the primary way of informing, advertising, exchanging opinions and expressing feelings. Posts and comments under each post shape public opinion on different but important issues making social media’s role in public life crucial. It has been observed that people's opinions expressed through social networks are more direct and representative than those expressed in face-to-face communication. Data shared on social media is a cornerstone of research because patterns of social behavior can be extracted that can be used for government, social, and business decisions. When an event breaks out, social networks are flooded with posts and comments, which are almost impossible for someone to read all of them. A system that would generate summarization of social media contents is necessary. Recent years have shown that abstract summarization combined with transfer learning and transformers has achieved excellent results in the field of text summarization, producing more human-like summaries. In this paper, a presentation of text summarization methods is first presented, as well as a review of text summarization systems. Finally, a system based on the pre-trained T5 model is described to generate summaries from user comments on social media.

Text Summarization in the Shona Language using Natural Language Processing

The rise of digital information in many languages, including Shona, highlights the significance of developing effective text summarizing techniques to promote information accessibility and usability. This work fills a major gap in the natural language processing (NLP) for the Shona language, which is widely spoken in Zimbabwe and its surrounding areas but has received little attention. The lack of pre-trained language models specifically designed for Shona, the intricacy of Shona's morphology, and the scarcity of annotated datasets provide the main obstacles to Shona text summarization.[1] The goal of this research is to create and modify contemporary machine learning methods for efficient Shona text summarizing in order to address these issues. By gathering and analyzing texts from a variety of sources, such as news stories, scholarly papers, and social media, we produced large annotated corpora. These datasets were utilized to fine-tune existing NLP models, such as Transformer-based architectures, ensuring they account for Shona’s specific language traits. To address the morphological and syntactic complexities of Shona, our solution combines statistical and rule-based techniques. When compared to baseline methods, the results show a significant improvement in the relevancy and accuracy of Shona text summaries. In addition to serving as a starting point for further NLP research in underrepresented languages, the generated models help Shona-speaking people in the areas of business, education, and media. By encouraging inclusivity and linguistic variety, showcasing the possibility for cross- lingual breakthroughs, and emphasizing the ethical development of technology, this research adds to the larger area of NLP.

BABYFYING MUSEUM TEXTS FOR YOUNGER VISITORS THROUGH AUGMENTED REALITY

Museums have long been recognized as a place to study historical objects. However, at the same time, museums are considered unfamiliar to younger visitors due to the complexity of the descriptions of their artifacts. In order to overcome this problem, this research attempted to simplify the museum texts through an application named babyfication. The process of simplification involves three main components: conceptual simplification, modification for simplicity, and summarization of text. For enhancing the comprehension of the museum texts, augmented reality (AR) technology features are utilized in the process. Through a combination of simplified language, vibrant visuals, and interactive elements, this research tried to prove the beneficial outcomes from AR technology in bridging the gap between traditional museum texts and the developmental needs of young children, particularly dealing with their engagement with cultural artifacts. A qualitative research approach and Spradley’s data analysis technique were employed to figure out how the descriptive texts of museum artifacts are babyfied. Applying the theory of babyfication from Purnomo et al. (2021) as a ludic adaptation concept, it was revealed that textual, visual, and operative elements, that have been babyfied by using AR technology as a medium, tend to be textually simpler, more visually appealing, and make it easier for children to understand the information.

Towards Trustworthy AI-Enabled Decision Support Systems: Validation of the Multisource AI Scorecard Table (MAST)

The Multisource AI Scorecard Table (MAST) is a checklist tool to inform the design and evaluation of trustworthy AI systems based on the U.S. Intelligence Community’s analytic tradecraft standards. In this study, we investigate whether MAST can be used to differentiate between high and low trustworthy AI-enabled decision support systems (AI-DSSs). Evaluating trust in AI-DSSs poses challenges to researchers and practitioners. These challenges include identifying the components, capabilities, and potential of these systems, many of which are based on the complex deep learning algorithms that drive DSS performance and preclude complete manual inspection. Using MAST, we developed two interactive AI-DSS testbeds. One emulated an identity-verification task in security screening, and another emulated a text-summarization system to aid in an investigative task. Each testbed had one version designed to reach low MAST ratings, and another designed to reach high MAST ratings. We hypothesized that MAST ratings would be positively related to the trust ratings of these systems. A total of 177 subject-matter experts were recruited to interact with and evaluate these systems. Results generally show higher MAST ratings for the high-MAST compared to the low-MAST groups, and that measures of trust perception are highly correlated with the MAST ratings. We conclude that MAST can be a useful tool for designing and evaluating systems that will engender trust perceptions, including for AI-DSS that may be used to support visual screening or text summarization tasks. However, higher MAST ratings may not translate to higher joint performance, and the connection between MAST and appropriate trust or trustworthiness remains an open question.

Automatic notes based on video records of online meetings using the latent Dirichlet allocation method

Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contain essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more human resources to change the recording of the meeting file. Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the latent Dirichlet allocation (LDA) method to determine text summarization and topic modeling. Based on this research, the system calculation using the LDA method produces a pretty good accuracy value for text summarization of 57.91% and topic modeling with a coherence score of 64.56%. Based on this research, the implementation of the latent Dirichlet allocation method for text summarization and topic modeling provides a fairly good level of similarity accuracy when compared to the minutes that are written manually and can be implemented in the Information Technology Department.

Optimized automated testing: test case generation and maintenance using latent semantic analysis-based TextRank and particle swarm optimization algorithms

Software development would have to include automated testing to ensure the finished product and performs as intended. However, the process of Test Case Generation and Maintenance can be time-consuming and error-prone, especially when manual methods are used. This research proposes a new approach to improve the efficiency and accuracy of automated testing using latent semantic analysis (LSA)-based TextRank (TR) and particle swarm optimization (PSO) algorithms. The study aims to evaluate the effectiveness of these algorithms in generating and optimizing test cases based on requirements analysis. To retrieve key information from the criteria, methods including text classification (TC), named entity recognition (NER), and sentiment analysis (SA) are used to evaluate the text. Test cases are then generated using LSA-based TR for text summarization and PSO for optimization. The aim of this work is to identify any limitations that need to be addressed and to evaluate the overall efficiency and accuracy of automated testing (AT) using proposed algorithms. The results of this research are expected to have important implications for the software industry, helps to improve the overall efficiency and accuracy of AT. The findings could guide future research that led to the creation of more advanced and effective tools for AT.

An overview of current issues in automatic text summarization of natural language using artificial intelligence methods

An overview of current issues in automatic text summarization of natural language using artificial intelligence methods