Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
When Neutral Summaries are not that Neutral: Quantifying Political Neutrality in LLM-Generated News Summaries
In an era where societal narratives are increasingly shaped by algorithmic curation, investigating the political neutrality of LLMs is an important research question. This study presents a fresh perspective on quantifyin…
Abstractive Text SummarizationArticlesText SummarizationExtra Global Attention Designation Using Keyword Detection in Sparse Transformer Architectures
In this paper, we propose an extension to Longformer Encoder-Decoder, a popular sparse transformer architecture. One common challenge with sparse transformers is that they can struggle with encoding of long range context…
Abstractive Text SummarizationDecoderL3Cube-MahaSum: A Comprehensive Dataset and BART Models for Abstractive Text Summarization in Marathi
We present the MahaSUM dataset, a large-scale collection of diverse news articles in Marathi, designed to facilitate the training and evaluation of models for abstractive summarization tasks in Indic languages. The datas…
Abstractive Text SummarizationArticlesText SummarizationA Novel LLM-based Two-stage Summarization Approach for Long Dialogues
Long document summarization poses a significant challenge in natural language processing due to input lengths that exceed the capacity of most state-of-the-art pre-trained language models. This study proposes a hierarchi…
Abstractive Text SummarizationDocument SummarizationMitigating the Impact of Reference Quality on Evaluation of Summarization Systems with Reference-Free Metrics
Automatic metrics are used as proxies to evaluate abstractive summarization systems when human annotations are too expensive. To be useful, these metrics should be fine-grained, show a high correlation with human annotat…
Abstractive Text SummarizationVideo Summarization Techniques: A Comprehensive Review
The rapid expansion of video content across a variety of industries, including social media, education, entertainment, and surveillance, has made video summarization an essential field of study. The current work is a sur…
Abstractive Text SummarizationExtractive SummarizationVideo SummarizationPersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models
With the rapid advancement of Natural Language Processing in recent years, numerous studies have shown that generic summaries generated by Large Language Models (LLMs) can sometimes surpass those annotated by experts, su…
Abstractive Text SummarizationArticlesIn-Context LearningSpecificityCriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation
Existing automatic prompt engineering methods are typically designed for discriminative tasks, where new task prompts are iteratively refined with limited feedback from a single metric reflecting a single aspect. However…
Abstractive Text SummarizationHallucinationPrompt EngineeringSentence+2Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization
Large language models (LLMs) can generate fluent summaries across domains using prompting techniques, reducing the need to train models for summarization applications. However, crafting effective prompts that guide LLMs …
Abstractive Text SummarizationHallucinationSentenceText SummarizationAbstractive Summarization of Low resourced Nepali language using Multilingual Transformers
Automatic text summarization in Nepali language is an unexplored area in natural language processing (NLP). Although considerable research has been dedicated to extractive summarization, the area of abstractive summariza…
Abstractive Text SummarizationArticlesExtractive SummarizationHeadline Generation+2Model-based Preference Optimization in Abstractive Summarization without Human Feedback
In abstractive summarization, the challenge of producing concise and accurate summaries arises from the vast amount of information contained in the source document. Consequently, although Large Language Models (LLMs) can…
Abstractive Text SummarizationTowards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis
Patents are the currency of innovation, and like any currency, they need to be managed and protected (Gavin Potenza). Patents, as legal documents that secure intellectual property rights, play a critical role in technolo…
Abstractive Text SummarizationKnowledge GraphsManagementPatent classification+3BERT-VBD: Vietnamese Multi-Document Summarization Framework
In tackling the challenge of Multi-Document Summarization (MDS), numerous methods have been proposed, spanning both extractive and abstractive summarization techniques. However, each approach has its own limitations, mak…
Abstractive Text SummarizationDocument SummarizationMulti-Document SummarizationTripletExtract-and-Abstract: Unifying Extractive and Abstractive Summarization within Single Encoder-Decoder Framework
Extract-then-Abstract is a naturally coherent paradigm to conduct abstractive summarization with the help of salient information identified by the extractive model. Previous works that adopt this paradigm train the extra…
Abstractive Text SummarizationDecoderAbstractive Text Summarization: State of the Art, Challenges, and Improvements
Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenge…
Abstractive Text SummarizationDocument SummarizationText SummarizationReinforcement Learning without Human Feedback for Last Mile Fine-Tuning of Large Language Models
Reinforcement learning is used to align language models with human preference signals after first pre-training the model to predict the next token of text within a large corpus using likelihood maximization. Before being…
Abstractive Text SummarizationImitation LearningModel Optimizationreinforcement-learning+1uMedSum: A Unified Framework for Advancing Medical Abstractive Summarization
Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce confabulations when prioritizing informat…
Abstractive Text SummarizationIn-Context LearningInformativenessBioLay_AK_SS at BioLaySumm: Domain Adaptation by Two-Stage Fine-Tuning of Large Language Models used for Biomedical Lay Summary Generation
Lay summarization is essential but challenging, as it simplifies scientific information for non-experts and keeps them updated with the latest scientific knowledge. In our participation in the Shared Task: Lay Summarizat…
Abstractive Text SummarizationArticlesDomain AdaptationLay SummarizationCTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization
Cyber Threat Intelligence (CTI) summarization task requires the system to generate concise and accurate highlights from raw intelligence data, which plays an important role in providing decision-makers with crucial infor…
Abstractive Text SummarizationMovieSum: An Abstractive Summarization Dataset for Movie Screenplays
Movie screenplay summarization is challenging, as it requires an understanding of long input contexts and various elements unique to movies. Large language models have shown significant advancements in document summariza…
Abstractive Text SummarizationDocument SummarizationLong-Form Narrative Summarization