Unsupervised Sentence Embeddings for Answer Summarization in Non-factoid CQA

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.