Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Abstractive Summarization Guided by Latent Hierarchical Document Structure
Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. This structure is essential to integrate and consolidate information f…
Abstractive Text SummarizationDecoderGraph Neural NetworkSentenceSummarizing Community-based Question-Answer Pairs
Community-based Question Answering (CQA), which allows users to acquire their desired information, has increasingly become an essential component of online services in various domains such as E-commerce, travel, and dini…
Abstractive Text SummarizationQuestion AnsweringSentenceED-FAITH: Evaluating Dialogue Summarization on Faithfulness
Abstractive summarization models typically generate content unfaithful to the input, thus highlighting the significance of evaluating the faithfulness of generated summaries. Most faithfulness metrics are only evaluated …
Abstractive Text SummarizationLanguage ModelingLanguage ModellingNovel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection
Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter. We present a pipelined ext…
Abstractive Text SummarizationExtractive SummarizationEvaluating and Improving Factuality in Multimodal Abstractive Summarization
Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modality and thus are not adequate for vision-a…
Abstractive Text SummarizationDocument SummarizationFRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the faithfulness of existing systems from a…
Abstractive Text SummarizationAutomatic Generation of Abstracts for Research Papers
Summarizing has always been an important utility for reading long documents. Research papers are unique in this regard, as they have a compulsory summary in the form of the abstract in the beginning of the document which…
Abstractive Text SummarizationDescriptiveQuestioning the Validity of Summarization Datasets and Improving Their Factual Consistency
The topic of summarization evaluation has recently attracted a surge of attention due to the rapid development of abstractive summarization systems. However, the formulation of the task is rather ambiguous, neither the l…
Abstractive Text SummarizationvalidHow Far are We from Robust Long Abstractive Summarization?
Abstractive summarization has made tremendous progress in recent years. In this work, we perform fine-grained human annotations to evaluate long document abstractive summarization systems (i.e., models and metrics) with …
Abstractive Text SummarizationImproving abstractive summarization with energy-based re-ranking
Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies…
Abstractive Text SummarizationRe-RankingAnalyzing Multi-Task Learning for Abstractive Text Summarization
Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of …
Abstractive Text SummarizationMulti-Task LearningNatural Language InferenceNatural Language Understanding+2CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization
Social media has increasingly played a key role in emergency response: first responders can use public posts to better react to ongoing crisis events and deploy the necessary resources where they are most needed. Timelin…
Abstractive Text SummarizationBenchmarkingUniversal Evasion Attacks on Summarization Scoring
The automatic scoring of summaries is important as it guides the development of summarizers. Scoring is also complex, as it involves multiple aspects such as fluency, grammar, and even textual entailment with the source …
Abstractive Text SummarizationDocument SummarizationNatural Language InferenceMutual Information Alleviates Hallucinations in Abstractive Summarization
Despite significant progress in the quality of language generated from abstractive summarization models, these models still exhibit the tendency to hallucinate, i.e., output content not supported by the source document. …
Abstractive Text SummarizationSalience Allocation as Guidance for Abstractive Summarization
Abstractive summarization models typically learn to capture the salient information from scratch implicitly. Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints o…
Abstractive Text SummarizationArticlesCorrecting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
Abstractive summarization models often generate inconsistent summaries containing factual errors or hallucinated content. Recent works focus on correcting factual errors in generated summaries via post-editing. Such corr…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingTowards Summary Candidates Fusion
Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full poten…
Abstractive Text SummarizationRe-RankingLegal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation
Summarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform when ap…
Abstractive Text SummarizationDocument SummarizationOpen-Ended Question AnsweringSaliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more acces…
Abstractive Text SummarizationFeature ImportanceSentiment Analysistext-classification+2Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most popular of which are question answering (…
Abstractive Text SummarizationQuestion AnsweringQuestion GenerationQuestion-Generation