Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze
Improving factual consistency in abstractive summarization has been a focus of current research. One promising approach is the post-editing method. However, previous works have yet to make sufficient use of factual facto…
Abstractive Text SummarizationSource Identification in Abstractive Summarization
Neural abstractive summarization models make summaries in an end-to-end manner, and little is known about how the source information is actually converted into summaries. In this paper, we define input sentences that con…
Abstractive Text SummarizationSentenceA Hybrid Strategy for Chat Transcript Summarization
Text summarization is the process of condensing a piece of text to fewer sentences, while still preserving its content. Chat transcript, in this context, is a textual copy of a digital or online conversation between a cu…
Abstractive Text SummarizationText SummarizationLOCOST: State-Space Models for Long Document Abstractive Summarization
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies. We propose LOCOST: an encoder-decoder architecture based on state-space models for con…
Abstractive Text SummarizationBook summarizationConditional Text GenerationDecoder+2GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres
As NLP models become increasingly capable of understanding documents in terms of coherent entities rather than strings, obtaining the most salient entities for each document is not only an important end task in itself bu…
Abstractive Text Summarizationcoreference-resolutionCoreference ResolutionHallucination+2MedTSS: transforming abstractive summarization of scientific articles with linguistic analysis and concept reinforcement
This research addresses the limitations of pretrained models (PTMs) in generating accurate and comprehensive abstractive summaries for scientific articles, with a specific focus on the challenges posed by medical researc…
Abstractive Text SummarizationArticlesHallucinationText SimplificationEvaluating GPT-3.5's Awareness and Summarization Abilities for European Constitutional Texts with Shared Topics
Constitutions are foundational legal documents that underpin the governmental and societal structures. As such, they are a reflection of a nation's cultural and social uniqueness, but also contribute to establish topics …
Abstractive Text SummarizationRevisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias
We characterize and study zero-shot abstractive summarization in Large Language Models (LLMs) by measuring position bias, which we propose as a general formulation of the more restrictive lead bias phenomenon studied pre…
Abstractive Text SummarizationDecoderPositionCross-Domain Robustness of Transformer-based Keyphrase Generation
Modern models for text generation show state-of-the-art results in many natural language processing tasks. In this work, we explore the effectiveness of abstractive text summarization models for keyphrase selection. A li…
Abstractive Text SummarizationKeyphrase ExtractionKeyphrase GenerationText Generation+2ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks
This study examines 4-bit quantization methods like GPTQ in large language models (LLMs), highlighting GPTQ's overfitting and limited enhancement in Zero-Shot tasks. While prior works merely focusing on zero-shot measure…
Abstractive Text SummarizationCode GenerationQuantizationExploiting Representation Bias for Data Distillation in Abstractive Text Summarization
Abstractive text summarization is surging with the number of training samples to cater to the needs of the deep learning models. These models tend to exploit the training data representations to attain superior performan…
Abstractive Text SummarizationDiversityText SummarizationFREDSum: A Dialogue Summarization Corpus for French Political Debates
Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved the performance of abstractive summarization systems. The majority of research has focused on wr…
Abstractive Text SummarizationDecoderQuestioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?
The evolution of legal datasets and the advent of large language models (LLMs) have significantly transformed the legal field, particularly in the generation of case judgment summaries. However, a critical concern arises…
Abstractive Text SummarizationDecision MakingFairnessAMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation
Ensuring factual consistency is crucial for natural language generation tasks, particularly in abstractive summarization, where preserving the integrity of information is paramount. Prior works on evaluating factual cons…
Abstractive Text SummarizationNatural Language InferenceText GenerationInvestigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
Despite the remarkable performance of generative large language models (LLMs) on abstractive summarization, they face two significant challenges: their considerable size and tendency to hallucinate. Hallucinations are co…
Abstractive Text SummarizationHallucinationHallucination EvaluationFair Abstractive Summarization of Diverse Perspectives
People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and politics. A fair summary should provide a c…
Abstractive Text SummarizationFairnessGreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization
Text summarization (TS) is a natural language processing (NLP) subtask pertaining to the automatic formulation of a concise and coherent summary that covers the major concepts and topics from one or multiple documents. R…
Abstractive Text SummarizationArticlesNews SummarizationText SummarizationControllable Topic-Focused Abstractive Summarization
Controlled abstractive summarization focuses on producing condensed versions of a source article to cover specific aspects by shifting the distribution of generated text towards a desired style, e.g., a set of topics. Su…
Abstractive Text SummarizationLegal-HNet: Mixing Legal Long-Context Tokens with Hartley Transform
Since its introduction, the transformers architecture has seen great adoption in NLP applications, but it also has limitations. Although the self-attention mechanism allows for generating very rich representations of the…
Abstractive Text SummarizationDecoderText SummarizationCorrection with Backtracking Reduces Hallucination in Summarization
Abstractive summarization aims at generating natural language summaries of a source document that are succinct while preserving the important elements. Despite recent advances, neural text summarization models are known …
Abstractive Text SummarizationHallucinationText Summarization