Toward Building an Academic Search Engine Understanding the Purposes of the Matched Sentences in an Abstract

Li Yuan Hsu, Chia Hao Kao, I. Sheng Jheng, Hung Hsuan Chen

Research output: Contribution to journalArticlepeer-review

Abstract

This paper introduces an automatic approach to understand the purposes of each sentence in the abstract of an academic document. Specifically, computers can label each sentence in the abstract as being related to one or several of six aspects - 'BACKGROUND', 'OBJECTIVES', 'METHODS', 'RESULTS', 'CONCLUSIONS', and 'OTHERS'. Experimental results obtained on a real dataset show that the labeling methodology outperforms baseline methods. We also build a prototype academic search engine to demonstrate the use of this new design. Users may search for articles containing keywords related to any of these six aspects to better meet their search goals.

Original languageEnglish
Article number9504549
Pages (from-to)109344-109354
Number of pages11
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Bidirectional LSTM
  • document understanding
  • hierarchical LSTM
  • specialty search engine

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