Abstractive text summarization is a task in natural language processing that automatically generates the summary from the source document in a human-written form with minimal loss of information. Research in text summarization has shifted towards ...

Factual inconsistency with source documents in automatically generated summaries can lead to misinformation or pose risks. Existing factual consistency (FC) metrics are constrained by their performance, efficiency, and explainability. Recent ...

• TreatFact: a clinical summary dataset annotated for factual consistency.
• Benchmarking 11 LLMs across news and clinical domains for FC evaluation.
• Analysis of key factors impacting LLM performance on factual consistency evaluation.

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 ...

Common law courts need to refer to similar precedents’ judgments to inform their current decisions. Generating high-quality summaries of court judgment documents can facilitate legal practitioners to efficiently review previous cases and assist ...

Automatic Text Summarization (ATS) is a research area that originated in the late 1950s and has gained increasing importance with the surging amount of text data available today. One of the key challenges in this area is how to quantitatively assess the ...

Automatic Text Summarization (ATS) is a research area that originated in the late 1950s and has gained increasing importance with the surge of text data available today. ATS approaches are generally classified into extractive and abstractive methods. ...

Automatic Text Summarization (ATS) involves estimating the salience of information and creating coherent summaries that include all relevant and important information from the original text. Extensive research has been carried out on ATS since ...

• Analyze the latest deep learning-based text summarization techniques.
• Introduce a new taxonomy of deep learning models for text summarization.
• Identify methods for representing input text clearly.
• Identify training strategies ...

Text summarization is a widely-researched problem among scholars in the field of natural language processing. Multiple techniques have been proposed to help tackle this problem, yet some of these methodologies may still exhibit limitations such ...

• Bi-LSTM and Meta-learning produce better results compared to other popular models.
• Meta-learning can be utilized to improve performance in data-scarce scenarios.
• Use SHAP, regression, decision tree, and text mutation to enhance ...

It is important for abstractive summarization models to understand the important parts of the original document and create a natural summary accordingly. Recently, studies have been conducted to incorporate important parts of the original document ...

Text summarization in medical domain is one of the most crucial chores as it deals with the critical human information. Consequently the proper summarization and key point extraction from medical deeds using pre-trained Language models is now the ...

• Presents a method to summarize HDS by parameter-efficient fine-tuning to a LLM.
• It utilizes QLoRA fine-tuning on the LLAMA 2 to minimize the memory requirements.
• A comparative analysis of prompt engineering and parameter-efficient ...

The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. ...

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• The adoption of MODE/D and the WS method to effectively tackle the optimization problem of extractive multi-document ATS.
• Employment of ROUGE metrics to gauge the quality of the proposed approach’s summaries relative to alternative ...

Automatic text summarization is more significant due to the rapid expansion of textual content on the web and in many archives, such as scientific papers, news items, legal documents, etc. Text summarization manually means it will consume more ...

Text summarization holds significance in the realm of natural language processing as it expedites the extraction of crucial information from extensive textual content. The paper presents an overview of six prevalent techniques for text ...

Automatic text summarization (ATS) technique is needed to create a summary comprising a compact version of significant details of the document. ATS models are used to generate an equivalent summary compared to the human created summary. It ...

Extractive text summarization involves selecting and combining key sentences directly from the original text, rather than generating new content. While various methods, both statistical and graph-based, have been explored for this purpose, ...

Software collaborative platforms, e.g., Gitter live chat and GitHub Discussions, are essential in software maintenance. Summarizing the live chat logs is useful for extracting, retrieving, and sharing knowledge for software developers. Automatic ...

• Dialog Disentanglement and summarization for software collaborative platform.
• Using the prompt tuning technique to exploit the knowledge of the pre-trained models for the summary tasks.
• Presenting manual annotation tool consisting ...

To relieve doctors from trivial recording, medical dialogue summarization (MDS) aims at automatically generating electronic health records (EHR) from the dialogues between doctors and patients. Having seen their remarkable performance on text/...

Text summarization is an essential task in natural language processing, and researchers have developed various approaches over the years, ranging from rule-based systems to neural networks. However, there is no single model or approach that ...

With the progress of the times, the ever-advancing and improving Internet technology and the ever-generating social media network platforms have made the amount of information in the network explode, which contains a massive scale of redundant ...

Automatic text summarization via representing a text as a graph has been investigated for over ten years. With the developments of attention mechanism and Transformer on natural language processing (NLP), it is possible to make a connection ...