Unsupervised Sentence Embeddings for Answer Summarization in Non-factoid CQA

Authors

  • Thi-Thanh Ha Ha Noi University of Science and Technology, and Thai Nguyen University of Information and Communication Technology, VietNam
  • Thanh-Chinh Nguyen Ha Noi University of Science and Technology, VietNam
  • Kiem-Hieu Nguyen Ha Noi University of Science and Technology, VietNam
  • Van-Chung Vu Ha Noi University of Science and Technology, VietNam
  • Kim-Anh Nguyen Ha Noi University of Science and Technology, VietNam

DOI:

https://doi.org/10.13053/cys-22-3-3027

Keywords:

Summarizing answers, non-factoid questions, multi-documment summarization, community question-answering, auto encoder, LSTM

Abstract

This paper presents a method for summarizing answers in Community Question Answering.We explore deep Auto-encoder and Long-short-termmemory Auto-encoder for sentence representation. The sentence representations are used to measure similarity in Maximal Marginal Relevance algorithm for extractive summarization. Experimental results on a benchmark dataset show that our unsupervised method achieves state-of-the-art performance while requiring no annotated data.

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Published

2018-09-25

Issue

Vol. 22 No. 3 (2018): Thematic issue on Natural Language Processing (Guest Editor: A. Gelbukh)

Section

Articles of the Thematic Issue

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