An answer summarization method based on keyword extraction

Abstract

Abstract. In order to reduce the redundancy of answer summary generated from community q&a dataset without topic tags, we propose an answer summarization algorithm based on keyword extraction. We combine tf-idf with word vector to change the influence transferred ratio equation in TextRank. And then during summarizing, we take the ratio of the number of sentences containing any keyword to the total number of candidate sentences as an adaptive factor for AMMR. Meanwhile we reuse the scores of keywords generated by TextRank as a weight factor for sentence similarity computing. Experimental results show that the proposed answer summarization is better than the traditional MMR and AMMR.

Which is Better for Deep Learning: Python or MATLAB? Answering Comparative Questions in Natural Language

“…graph-based (Litvak and Last, 2008) and neural models (Isonuma et al, 2019;Coavoux et al, 2019). A common approach to summarization is based on the TextRank graph algorithm (Mihalcea, 2004;Fan and Fang, 2017).…”