Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting. Previous work aimed to enhance content selection and factuality t…
Abstractive Text SummarizationPrompting Large Language Models with Speech Recognition Abilities
Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. In this paper we extend the capabilities …
Abstractive Text SummarizationAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Open-Ended Question Answering+3Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates r…
Abstractive Text SummarizationContrastive LearningNamed Entity Inclusion in Abstractive Text Summarization
We address the named entity omission - the drawback of many current abstractive text summarizers. We suggest a custom pretraining objective to enhance the model's attention on the named entities in a text. At first, the …
Abstractive Text Summarizationnamed-entity-recognitionNamed Entity RecognitionText SummarizationChallenges in Domain-Specific Abstractive Summarization and How to Overcome them
Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as domain-specific abstractive text summariz…
Abstractive Text SummarizationHallucinationText SummarizationFusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents
The realm of scientific text summarization has experienced remarkable progress due to the availability of annotated brief summaries and ample data. However, the utilization of multiple input modalities, such as videos an…
Abstractive Text SummarizationText SummarizationAbstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM
Text summarization is a fundamental task in natural language processing that aims to condense large amounts of textual information into concise and coherent summaries. With the exponential growth of content and the need …
Abstractive Text SummarizationText SummarizationGUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization
Automatic summarization with pre-trained language models has led to impressively fluent results, but is prone to 'hallucinations', low performance on non-news genres, and outputs which are not exactly summaries. Targetin…
Abstractive Text SummarizationOpen-Domain Text Evaluation via Contrastive Distribution Methods
Recent advancements in open-domain text generation, driven by the power of large pre-trained language models (LLMs), have demonstrated remarkable performance. However, assessing these models' generation quality remains a…
Abstractive Text SummarizationCoherence EvaluationText GenerationAbsformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization
Multi-document summarization (MDS) refers to the task of summarizing the text in multiple documents into a concise summary. The generated summary can save the time of reading many documents by providing the important con…
Abstractive Text SummarizationDecoderDocument SummarizationLanguage Modeling+4Towards End-to-end Speech-to-text Summarization
Speech-to-text (S2T) summarization is a time-saving technique for filtering and keeping up with the broadcast news uploaded online on a daily basis. The rise of large language models from deep learning with impressive te…
Abstractive Text SummarizationSpeech-to-TextText GenerationText Summarization+1How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?
Automatic summarization of legal case judgements has traditionally been attempted by using extractive summarization methods. However, in recent years, abstractive summarization models are gaining popularity since they ca…
Abstractive Text SummarizationExtractive SummarizationText SummarizationUnsupervised Extractive Summarization of Emotion Triggers
Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoing disasters. Recent approaches trained s…
Abstractive Text SummarizationExtractive SummarizationUnsupervised Extractive SummarizationTowards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
We propose a simple approach for the abstractive summarization of long legal opinions that considers the argument structure of the document. Legal opinions often contain complex and nuanced argumentation, making it chall…
Abstractive Text SummarizationRerankingAn abstractive text summarization technique using transformer model with self-attention mechanism
Creating a summarized version of a text document that still conveys precise meaning is an incredibly complex endeavor in natural language processing (NLP). Abstract text summarization (ATS) is the process of using facts …
Abstractive Text SummarizationText SummarizationIDAS: Intent Discovery with Abstractive Summarization
Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents. We show that recent competitive methods in in…
Abstractive Text SummarizationDescriptiveIn-Context LearningIntent Discovery+3Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon is emphasized in tasks like summarization…
Abstractive Text SummarizationNatural Language Inferencereinforcement-learningReinforcement Learning+1Abstractive Summarization as Augmentation for Document-Level Event Detection
Transformer-based models have consistently produced substantial performance gains across a variety of NLP tasks, compared to shallow models. However, deep models are orders of magnitude more computationally expensive tha…
Abstractive Text SummarizationEvent DetectionText GenerationText SummarizationDomain Aligned Prefix Averaging for Domain Generalization in Abstractive Summarization
Domain generalization is hitherto an underexplored area applied in abstractive summarization. Moreover, most existing works on domain generalization have sophisticated training algorithms. In this paper, we propose a lig…
Abstractive Text SummarizationDomain GeneralizationIncorporating Distributions of Discourse Structure for Long Document Abstractive Summarization
For text summarization, the role of discourse structure is pivotal in discerning the core content of a text. Regrettably, prior studies on incorporating Rhetorical Structure Theory (RST) into transformer-based summarizat…
Abstractive Text SummarizationText Summarization