%0 Conference Proceedings
%T Context Aware Contrastive Opinion Summarization
%+ Jerusalem College of Engineering [Inde]
%+ St Joseph's College Of Engineering
%A Lavanya, S., K.
%A Parvathavarthini, B.
%Z Part 1: Computational Intelligence for Text Analysis
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 3rd International Conference on Computational Intelligence in Data Science (ICCIDS)
%C Chennai, India
%Y Aravindan Chandrabose
%Y Ulrich Furbach
%Y Ashish Ghosh
%Y Anand Kumar M.
%I Springer International Publishing
%3 Computational Intelligence in Data Science
%V AICT-578
%P 16-29
%8 2020-02-20
%D 2020
%R 10.1007/978-3-030-63467-4_2
%K Opinion summarization
%K Attention model
%K Pointer-generator network
%K Context-aware sentiment classification
%Z Computer Science [cs]Conference papers
%X Model-based approaches for context-sensitive contrastive summarization depend on hand-crafted features for producing a summary. Deriving these hand-crafted features using machine learning algorithms is computationally expensive. This paper presents a deep learning approach to provide an end-to-end solution for context-sensitive contrastive summarization. A hierarchical attention model referred to as Contextual Sentiment LSTM (CSLSTM) is proposed to automatically learn the representations of context, feature and opinion words present in review documents of each entity. The resultant document context vector is a high-level representation of the document. It is used as a feature for context-sensitive classification and summarization. Given a set of summaries from positive class and a negative class of two entities, the summaries which have high contrastive score are identified and presented as context-sensitive contrastive summaries. Experimental results on restaurant dataset show that the proposed model achieves better performance than the baseline models.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-03434805/document
%2 https://inria.hal.science/hal-03434805/file/507484_1_En_2_Chapter.pdf
%L hal-03434805
%U https://inria.hal.science/hal-03434805
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICCIDS
%~ IFIP-AICT-578