Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality
Improving factual consistency of abstractive summarization has been a widely studied topic. However, most of the prior works on training factuality-aware models have ignored the negative effect it has on summary quality.…
Abstractive Text SummarizationContrastive LearningFourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length. Many r…
Abstractive Text SummarizationDocument SummarizationLong-range modelingOpen-Domain Question AnsweringSegmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
Transformers have shown dominant performance across a range of domains including language and vision. However, their computational cost grows quadratically with the sequence length, making their usage prohibitive for res…
Abstractive Text SummarizationText SummarizationUSB: A Unified Summarization Benchmark Across Tasks and Domains
While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability. We introduce a Wiki…
Abstractive Text SummarizationArticlesExtractive SummarizationSentenceAbstractive Text Summarization Using the BRIO Training Paradigm
Summary sentences produced by abstractive summarization models may be coherent and comprehensive, but they lack control and rely heavily on reference summaries. The BRIO training paradigm assumes a non-deterministic dist…
Abstractive Text SummarizationText SummarizationLarge Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization
With the recent undeniable advancement in reasoning abilities in large language models (LLMs) like ChatGPT and GPT-4, there is a growing trend for using LLMs on various tasks. One area where LLMs can be employed is as an…
Abstractive Text SummarizationTask-agnostic Distillation of Encoder-Decoder Language Models
Finetuning pretrained language models (LMs) have enabled appealing performance on a diverse array of tasks. The intriguing task-agnostic property has driven a shifted focus from task-specific to task-agnostic distillatio…
Abstractive Text SummarizationDecoderCounterfactual Debiasing for Generating Factually Consistent Text Summaries
Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved. In …
Abstractive Text SummarizationcounterfactualText SummarizationBalancing Lexical and Semantic Quality in Abstractive Summarization
An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some perf…
Abstractive Text SummarizationRe-RankingSemantic SimilaritySemantic Textual SimilarityThe Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation
End-to-end spoken language understanding (SLU) remains elusive even with current large pretrained language models on text and speech, especially in multilingual cases. Machine translation has been established as a powerf…
Abstractive Text SummarizationContinual Learningintent-classificationIntent Classification+5PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization
Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance. This work propos…
Abstractive Text SummarizationThe Current State of Summarization
With the explosive growth of textual information, summarization systems have become increasingly important. This work aims to concisely indicate the current state of the art in abstractive text summarization. As part of …
Abstractive Text SummarizationDecoderText SummarizationHistAlign: Improving Context Dependency in Language Generation by Aligning with History
Language models (LMs) can generate hallucinations and incoherent outputs, which highlights their weak context dependency. Cache-LMs, which augment LMs with a memory of recent history, can increase context dependency and …
Abstractive Text SummarizationText GenerationLearning Summary-Worthy Visual Representation for Abstractive Summarization in Video
Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcri…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingPersonalized Abstractive Summarization by Tri-agent Generation Pipeline
Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we propose a tri-agent generation pipeline com…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingLarge Language ModelEntity-Based Evaluation of Political Bias in Automatic Summarization
Growing literature has shown that NLP systems may encode social biases; however, the political bias of summarization models remains relatively unknown. In this work, we use an entity replacement method to investigate the…
Abstractive Text SummarizationArticlesCan LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with the standard pre-training corpora and fi…
Abstractive Text SummarizationText SummarizationLift Yourself Up: Retrieval-augmented Text Generation with Self Memory
With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text generation tasks. Since better memory would typically prompt better generation~(w…
Abstractive Text SummarizationDialogue GenerationMachine TranslationRetrieval+3Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization
Most Transformer based abstractive summarization systems have a severe mismatch between training and inference, i.e., exposure bias. From diverse perspectives, we introduce a simple multi-level contrastive learning frame…
Abstractive Text SummarizationContrastive LearningDecoderFew-Shot LearningGEMINI: Controlling the Sentence-level Writing Style for Abstractive Text Summarization
Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible …
Abstractive Text SummarizationSentenceSentence ReWritingText Summarization