TY  - JOUR
T1  - Document Summarization Considering Entailment Relation between Sentences
AU  - Kwon, Youngdae 
AU  - Kim, Noo-ri 
AU  - Lee, Jee-Hyong 
JO  - Journal of KIISE, JOK
PY  - 2017
DA  - 2017/1/14
DO  - 
KW  - document summarization
KW  - entailment relation inference
KW  - natural language processing
KW  - TextRank
KW  - recurrent neural network
AB  - Document summarization aims to generate a summary that is consistent and contains the highly related sentences in a document. In this study, we implemented for document summarization that extracts highly related sentences from a whole document by considering both similarities and entailment relations between sentences. Accordingly, we proposed a new algorithm, TextRank-NLI, which combines a Recurrent Neural Network based Natural Language Inference model and a Graphbased ranking algorithm used in single document extraction-based summarization task. In order to evaluate the performance of the new algorithm, we conducted experiments using the same datasets as used in TextRank algorithm. The results indicated that TextRank-NLI showed 2.3% improvement in performance, as compared to TextRank.