Surveyor: A System for Generating Coherent Survey Articles for Scientific Topics

Authors

  • Rahul Jha University of Michigan
  • Reed Coke University of Michigan
  • Dragomir Radev University of Michigan

DOI:

https://doi.org/10.1609/aaai.v29i1.9495

Keywords:

Summarization, Multi-Document Summarization, Scientific Summarization, Coherent Summarization, Discourse Based Summarization

Abstract

We investigate the task of generating coherent survey articles for scientific topics. We introduce an extractive summarization algorithm that combines a content model with a discourse model to generate coherent and readable summaries of scientific topics using text from scientific articles relevant to the topic. Human evaluation on 15 topics in computational linguistics shows that our system produces significantly more coherent summaries than previous systems. Specifically, our system improves the ratings for coherence by 36% in human evaluation compared to C-Lexrank, a state of the art system for scientific article summarization.

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Published

2015-02-19

How to Cite

Jha, R., Coke, R., & Radev, D. (2015). Surveyor: A System for Generating Coherent Survey Articles for Scientific Topics. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9495

Issue

Section

Main Track: NLP and Knowledge Representation