Papers Abstractive Text Summarization
“Abstractive Text Summarization” 태그가 달린 논문 852편 · 필터 해제
Spurious Correlations in Reference-Free Evaluation of Text Generation
Model-based, reference-free evaluation metrics have been proposed as a fast and cost-effective approach to evaluate Natural Language Generation (NLG) systems. Despite promising recent results, we find evidence that refer…
Abstractive Text SummarizationText GenerationText SummarizationA Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization
Automatic summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) that represents the most important pieces of information in the original text. Existing summariza…
Abstractive Text SummarizationFactual Error Correction for Abstractive Summaries Using Entity Retrieval
Despite the recent advancements in abstractive summarization systems leveraged from large-scale datasets and pre-trained language models, the factual correctness of the summary is still insufficient. One line of trials t…
Abstractive Text SummarizationEntity RetrievalRetrievalFactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world ap…
Abstractive Text SummarizationARCPSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we designed a novel soft prompts architecture coupled with a prompt pre-training plus fine-tuning paradigm t…
Abstractive Text SummarizationDecoderText GenerationAbstractive summarization of hospitalisation histories with transformer networks
In this paper we present a novel approach to abstractive summarization of patient hospitalisation histories. We applied an encoder-decoder framework with Longformer neural network as an encoder and BERT as a decoder. Our…
Abstractive Text SummarizationDecoderImproving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization
While there has been recent progress in abstractive summarization as applied to different domains including news articles, scientific articles, and blog posts, the application of these techniques to clinical text summari…
Abstractive Text SummarizationArticlesDecoderText SummarizationBRIO: Bringing Order to Abstractive Summarization
Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the…
Abstractive Text SummarizationText SummarizationSpeculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding. Speculative Decoding has two innovations: Spe…
Abstractive Text SummarizationMachine TranslationTranslationEntity-driven Fact-aware Abstractive Summarization of Biomedical Literature
As part of the large number of scientific articles being published every year, the publication rate of biomedical literature has been increasing. Consequently, there has been considerable effort to harness and summarize …
Abstractive Text SummarizationArticlesDecoderDocument Summarization+1Linearizing Transformer with Key-Value Memory
Efficient transformer variants with linear time complexity have been developed to mitigate the quadratic computational overhead of the vanilla transformer. Among them are low-rank projection methods such as Linformer and…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingMachine Translation+2Towards Abstractive Grounded Summarization of Podcast Transcripts
Podcasts have recently shown a rapid rise in popularity. Summarization of podcast transcripts is of practical benefit to both content providers and consumers. It helps consumers to quickly decide whether they will listen…
Abstractive Text SummarizationAraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization
Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretrained on large corpora. While most existing …
Abstractive Text SummarizationDecoderNatural Language UnderstandingMeta-X$_{NLG}$: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation
Recently, the NLP community has witnessed a rapid advancement in multilingual and cross-lingual transfer research where the supervision is transferred from high-resource languages (HRLs) to low-resource languages (LRLs).…
Abstractive Text SummarizationCross-Lingual TransferMeta-LearningQuestion Generation+3ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE methods. We first show that a residual …
Abstractive Text SummarizationMachine TranslationTranslationAn Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models
We propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output. We show that the imitation learning alg…
Abstractive Text SummarizationImitation LearningMachine TranslationText Simplification+1Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Abstractive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text. We analyze the connection between hallucinations and training data, and fin…
Abstractive Text SummarizationDifferentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. A c…
Abstractive Text SummarizationExtractive SummarizationSummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset. These models are typical…
Abstractive Text SummarizationDocument SummarizationMixture-of-ExpertsRe-Ranking+1Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models. This advancement has resulted in more fluent, coherent and…
Abstractive Text SummarizationData-to-Text GenerationDialogue GenerationMachine Translation+1