Year: 2019
Type: conference-paper
Abstract: When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence.However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood.Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them.There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space.Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence.We conduct extensive experiments on both single-and multidocument summarization datasets and report findings on sentence selection and abstraction.
DOI: 10.18653/v1/p19-1209
Institutions: University of Central Florida, Adobe Systems (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2952215948