Abstract

Modeling the relations between text spans in a document is a crucial yet challenging problem for extractive summarization. Various kinds of relations exist among text spans of different granularity, such as discourse relations between elementary discourse units and coreference relations between phrase mentions. In this paper, we propose a heterogeneous graph based model for extractive summarization that incorporates both discourse and coreference relations. The heterogeneous graph contains three types of nodes, each corresponds to text spans of different granularity. Experimental results on a benchmark summarization dataset verify the effectiveness of our proposed method.

Highlights

  • Automatic summarization aims to condense the information of the input document into a shorter summary

  • We propose a novel heterogeneous graph based model for extractive summarization

  • The model predicts a sequence of binary labels Y = {y1, y2, ..., yn}, where yi = 1 indicates that the ith Elementary Discourse Units (EDUs) should be included in the summary

Read more

Summary

Introduction

Automatic summarization aims to condense the information of the input document into a shorter summary. The task has two main paradigms: extractive summarization and abstractive summarization. Generating summary sentences from scratch, abstractive summarizers can generate concise and flexible summaries. They suffer from the problem of not being able to reproduce factual details correctly (See et al, 2017). Extractive summarization aims to select salient text spans (mostly sentences) from the input document. Extractive summarizers have the advantage of being efficient and factually reliable.

Objectives
Methods
Results

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.