Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Summarization of Investment Reports Using Pre-trained Model
In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on f…
Abstractive Text SummarizationExtractive SummarizationManagementmodelAbstractive summarization from Audio Transcription
Currently, large language models are gaining popularity, their achievements are used in many areas, ranging from text translation to generating answers to queries. However, the main problem with these new machine learnin…
Abstractive Text SummarizationQuantizationBOND: Aligning LLMs with Best-of-N Distillation
Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that …
Abstractive Text SummarizationAdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that asses their abilities to easily adapt to different domains. We evaluate the domain adaptation…
Abstractive Text SummarizationDomain AdaptationIn-Context LearningText SummarizationA Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches
Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference time to generate summaries directly; however, such approaches often suffer from incon…
Abstractive Text SummarizationData AugmentationExtractive SummarizationExtractive Text Summarization+5STORYSUMM: Evaluating Faithfulness in Story Summarization
Human evaluation has been the gold standard for checking faithfulness in abstractive summarization. However, with a challenging source domain like narrative, multiple annotators can agree a summary is faithful, while mis…
Abstractive Text SummarizationApplicability of Large Language Models and Generative Models for Legal Case Judgement Summarization
Automatic summarization of legal case judgements, which are known to be long and complex, has traditionally been tried via extractive summarization models. In recent years, generative models including abstractive summari…
Abstractive Text SummarizationExtractive SummarizationQuery-Guided Self-Supervised Summarization of Nursing Notes
Nursing notes, an important part of Electronic Health Records (EHRs), track a patient's health during a care episode. Summarizing key information in nursing notes can help clinicians quickly understand patients' conditio…
Abstractive Text SummarizationDomain AdaptationHallucinationText SummarizationDiscrete Diffusion Language Model for Long Text Summarization
While diffusion models excel at conditional generating high-quality images, prior works in discrete diffusion models were not evaluated on conditional long-text generation. In this work, we address the limitations of pri…
Abstractive Text SummarizationDecoderLanguage ModelingLanguage Modelling+4OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
We introduce OpenDebateEvidence, a comprehensive dataset for argument mining and summarization sourced from the American Competitive Debate community. This dataset includes over 3.5 million documents with rich metadata, …
Abstractive Text SummarizationArgument MiningEMO-KNOW: A Large Scale Dataset on Emotion and Emotion-cause
Emotion-Cause analysis has attracted the attention of researchers in recent years. However, most existing datasets are limited in size and number of emotion categories. They often focus on extracting parts of the documen…
Abstractive Text SummarizationCADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization
Abstractive dialogue summarization is the task of distilling conversations into informative and concise summaries. Although reviews have been conducted on this topic, there is a lack of comprehensive work detailing the c…
Abstractive Dialogue SummarizationAbstractive Text SummarizationSystematic Literature ReviewCERET: Cost-Effective Extrinsic Refinement for Text Generation
Large Language Models (LLMs) are powerful models for generation tasks, but they may not generate good quality outputs in their first attempt. Apart from model fine-tuning, existing approaches to improve prediction accura…
Abstractive Text SummarizationQuestion AnsweringText GenerationInvestigating and Addressing Hallucinations of LLMs in Tasks Involving Negation
Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, they have been shown to suffer from a critical limitation pertinent to 'hallucination' in their …
Abstractive Text SummarizationDialogue GenerationHallucinationLogical Reasoning+3Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization
Nowadays, neural text generation has made tremendous progress in abstractive summarization tasks. However, most of the existing summarization models take in the whole document all at once, which sometimes cannot meet the…
Abstractive Text SummarizationStory GenerationText GenerationHeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew
While large language models (LLMs) excel in various natural language tasks in English, their performance in lower-resourced languages like Hebrew, especially for generative tasks such as abstractive summarization, remain…
Abstractive Text SummarizationSentenceText SummarizationAssessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing
Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot generation of abstractive summaries for given articles. However, little is known about the robustness of such a process of zero-shot su…
Abstractive Text SummarizationArticlesAspect-oriented Consumer Health Answer Summarization
Community Question-Answering (CQA) forums have revolutionized how people seek information, especially those related to their healthcare needs, placing their trust in the collective wisdom of the public. However, there ca…
Abstractive Text SummarizationCommunity Question AnsweringQuestion AnsweringQuestion SimilarityATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data
The abundance of situational information on Twitter poses a challenge for users to manually discern vital and relevant information during disasters. A concise and human-interpretable overview of this information helps de…
Abstractive Text SummarizationDisaster ResponseSentenceImproving Long Text Understanding with Knowledge Distilled from Summarization Model
Long text understanding is important yet challenging for natural language processing. A long article or document usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as n…
Abstractive Text SummarizationDocument ClassificationOpen-Domain Question AnsweringQuestion Answering+2