Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Active Learning for Abstractive Text Summarization
Construction of human-curated annotated datasets for abstractive text summarization (ATS) is very time-consuming and expensive because creating each instance requires a human annotator to read a long document and compose…
Abstractive Text SummarizationActive LearningDiversitySelf-Learning+3BUS: Efficient and Effective Vision-Language Pre-Training with Bottom-Up Patch Summarization.
Vision Transformer (ViT) based Vision-Language Pretraining (VLP) models recently demonstrated impressive performance in various tasks. However, the lengthy visual token sequences used in these models can lead to inef…
Abstractive Text SummarizationDecoderDocument SummarizationBART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization
The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in sum…
Abstractive Text SummarizationText SummarizationTextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ com…
Abstractive Text SummarizationData-to-Text GenerationDialogueMachine Translation+7Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. However, recent unsupervised models …
Abstractive Text Summarizationreinforcement-learningReinforcement Learning (RL)Sentence+2OpineSum: Entailment-based self-training for abstractive opinion summarization
A typical product or place often has hundreds of reviews, and summarization of these texts is an important and challenging problem. Recent progress on abstractive summarization in domains such as news has been driven by …
Abstractive Text SummarizationArticlesFew-Shot LearningNatural Language Inference+1Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
The evaluation of abstractive summarization models typically uses test data that is identically distributed as training data. In real-world practice, documents to be summarized may contain input noise caused by text extr…
Abstractive Text SummarizationEIT: Enhanced Interactive Transformer
Two principles: the complementary principle and the consensus principle are widely acknowledged in the literature of multi-view learning. However, the current design of multi-head self-attention, an instance of multi-vie…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingMachine Translation+2mFACE: Multilingual Summarization with Factual Consistency Evaluation
Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from gene…
Abstractive Text SummarizationImproving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences
Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsiste…
Abstractive Text SummarizationConversation SummarizationUnsupervised Summarization Re-ranking
With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised…
Abstractive Text SummarizationRe-RankingInverse Reinforcement Learning for Text Summarization
We introduce inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models, imitating human summarization behaviors. Our IRL model estimates the reward function using a suite…
Abstractive Text Summarizationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization
Multimodal abstractive summarization (MAS) aims to produce a concise summary given the multimodal data (text and vision). Existing studies mainly focus on how to effectively use the visual features from the perspective o…
Abstractive Text SummarizationAdvancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models
Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this p…
Abstractive Text SummarizationCross-Lingual Abstractive SummarizationDocument Level Machine TranslationMachine Translation+2Momentum Calibration for Text Generation
The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers. These models are usually t…
Abstractive Text SummarizationText GenerationText SummarizationImproved Beam Search for Hallucination Mitigation in Abstractive Summarization
Advancement in large pretrained language models has significantly improved their performance for conditional language generation tasks including summarization albeit with hallucinations. To reduce hallucinations, convent…
Abstractive Text SummarizationDiversityHallucinationNatural Language Inference+2Document-Level Abstractive Summarization
The task of automatic text summarization produces a concise and fluent text summary while preserving key information and overall meaning. Recent approaches to document-level summarization have seen significant improvemen…
Abstractive Text SummarizationRetrievalText SummarizationKATSum: Knowledge-aware Abstractive Text Summarization
Text Summarization is recognised as one of the NLP downstream tasks and it has been extensively investigated in recent years. It can assist people with perceiving the information rapidly from the Internet, including news…
Abstractive Text SummarizationArticlesText SummarizationCoP: Factual Inconsistency Detection by Controlling the Preference
Abstractive summarization is the process of generating a summary given a document as input. Although significant progress has been made, the factual inconsistency between the document and the generated summary still limi…
Abstractive Text SummarizationUnsupervised Opinion Summarisation in the Wasserstein Space
Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such …
Abstractive Text SummarizationDecoder