Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization
The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and document has become an important and urgent ta…
Abstractive Text SummarizationLanguage ModellingNatural Language InferenceQuestion AnsweringTowards Improving Faithfulness in Abstractive Summarization
Despite the success achieved in neural abstractive summarization based on pre-trained language models, one unresolved issue is that the generated summaries are not always faithful to the input document. There are two pos…
Abstractive Text SummarizationDecoderLanguage ModelingLanguage Modelling+1Probing of Quantitative Values in Abstractive Summarization Models
Abstractive text summarization has recently become a popular approach, but data hallucination remains a serious problem, including with quantitative data. We propose a set of probing tests to evaluate the efficacy of abs…
Abstractive Text SummarizationHallucinationText SummarizationLipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization
Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we intro…
Abstractive Text SummarizationDocument SummarizationSource-summary Entity Aggregation in Abstractive Summarization
In a text, entities mentioned earlier can be referred to in later discourse by a more general description. For example, Celine Dion and Justin Bieber can be referred to by Canadian singers or celebrities. In this work, w…
Abstractive Text SummarizationAbstractive Approaches To Multidocument Summarization Of Medical Literature Reviews
Text summarization has been a trending domain of research in NLP in the past few decades. The medical domain is no exception to the same. Medical documents often contain a lot of jargon pertaining to certain domains, and…
Abstractive Text SummarizationText SummarizationFaithful Abstractive Summarization via Fact-aware Consistency-constrained Transformer
Abstractive summarization is a classic task in Natural Language Generation (NLG), which aims to produce a concise summary of the original document. Recently, great efforts have been made on sequence-to-sequence neural ne…
Abstractive Text SummarizationText GenerationOut-of-Distribution Detection and Selective Generation for Conditional Language Models
Machine learning algorithms typically assume independent and identically distributed samples in training and at test time. Much work has shown that high-performing ML classifiers can degrade significantly and provide ove…
Abstractive Text SummarizationOut-of-Distribution DetectionOut of Distribution (OOD) DetectionCalibrating Sequence likelihood Improves Conditional Language Generation
Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible…
abstractive question answeringAbstractive Text SummarizationBlockingData-to-Text Generation+5COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary is always evaluated from summary-level wh…
Abstractive Text SummarizationContrastive LearningGPURe-Ranking+1Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization P…
Abstractive Text SummarizationSentenceSentence FusionA Step-by-Step Gradient Penalty with Similarity Calculation for Text Summary Generation
The summary generation model equipped with gradient penalty avoids overfitting and makes the model more stable. However, the traditional gradient penalty faces two issues: (i) calculating the gradient twice increases tra…
Abstractive Text SummarizationHarnessing Abstractive Summarization for Fact-Checked Claim Detection
Social media platforms have become new battlegrounds for anti-social elements, with misinformation being the weapon of choice. Fact-checking organizations try to debunk as many claims as possible while staying true to th…
Abstractive Text SummarizationFact CheckingMisinformationRetrieval+1Applying Transformer-based Text Summarization for Keyphrase Generation
Keyphrases are crucial for searching and systematizing scholarly documents. Most current methods for keyphrase extraction are aimed at the extraction of the most significant words in the text. But in practice, the list o…
Abstractive Text SummarizationKeyphrase ExtractionKeyphrase GenerationText SummarizationExtractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization
The problems of unfaithful summaries have been widely discussed under the context of abstractive summarization. Though extractive summarization is less prone to the common unfaithfulness issues of abstractive summaries, …
Abstractive Text SummarizationExtractive SummarizationEntity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
Despite the success of recent abstractive summarizers on automatic evaluation metrics, the generated summaries still present factual inconsistencies with the source document. In this paper, we focus on entity-level factu…
Abstractive Text SummarizationArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining
A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrati…
Abstractive Text SummarizationArgument MiningEnhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization
Contextualized word embeddings can lead to state-of-the-art performances in natural language understanding. Recently, a pre-trained deep contextualized text encoder such as BERT has shown its potential in improving natur…
Abstractive Dialogue SummarizationAbstractive Text SummarizationArticlesDecoder+5Entity-based De-noising Modeling for Controllable Dialogue Summarization
Although fine-tuning pre-trained backbones produces fluent and grammatically-correct text in various language generation tasks, factual consistency in abstractive summarization remains challenging. This challenge is espe…
Abstractive Text SummarizationHallucinationText GenerationTo Adapt or to Fine-tune: A Case Study on Abstractive Summarization
Recent advances in the field of abstractive summarization leverage pre-trained language models rather than train a model from scratch. However, such models are sluggish to train and accompanied by a massive overhead. Res…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingPretrained Multilingual Language Models