Structure-Infused Copy Mechanisms for Abstractive Summarization
Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.
Code (1)
Tasks
Abstractive Text SummarizationSentenceSimilar Papers 제목 키워드 기반
Controlling Decoding for More Abstractive Summaries with Copy-Based Networks
Attention-based neural abstractive summarization systems equipped with copy mechanisms have shown promising results. Despite this success, it has been noticed that such a system generates a summary by mostly, if not enti…
Abstractive Text SummarizationExtractive SummarizationVAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization
This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) a…
Abstractive Text SummarizationExtractive SummarizationSentenceText SummarizationControlling the Amount of Verbatim Copying in Abstractive Summarization
An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise…
Abstractive Text SummarizationLanguage ModelingLanguage ModellingText SummarizationSentence-level Planning for Especially Abstractive Summarization
Abstractive summarization models heavily rely on copy mechanisms, such as the pointer network or attention, to achieve good performance, measured by textual overlap with reference summaries. As a result, the generated su…
Abstractive Text SummarizationDecoderSentenceStructSum: Summarization via Structured Representations
Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer fro…
Abstractive Text SummarizationDecoderDocument SummarizationSentence+1