TY - JOUR
T1 - Topical sentence embedding for query focused document summarization
AU - Gao, Yang
AU - Wei, Linjing
AU - Huang, Heyan
AU - Liu, Qian
N1 - Publisher Copyright:
Copyright © by the paper's authors. Copying permitted for private and academic purposes.
PY - 2017
Y1 - 2017
N2 - Distributed vector representation for sentences have been utilized in summarization area, since it simplifies semantic cosine calculation between sentence to sentence as well as sentence to document. Many extension works have been done to incorporate latent topics and word embedding, however, few of them assign sentences with explicit topics. Besides, much sentence embedding framework follows the same spirit of prediction task about a word in the sentence, which omits the sentence-to-sentence coherence. To address these problems, we proposed a novel sentence embedding framework to collaborate the current sentence representation, word-based content and topic assignment of the sentence to predict the next sentence representation. The experiments on summarization tasks show our model outperforms state-of-the-art methods.
AB - Distributed vector representation for sentences have been utilized in summarization area, since it simplifies semantic cosine calculation between sentence to sentence as well as sentence to document. Many extension works have been done to incorporate latent topics and word embedding, however, few of them assign sentences with explicit topics. Besides, much sentence embedding framework follows the same spirit of prediction task about a word in the sentence, which omits the sentence-to-sentence coherence. To address these problems, we proposed a novel sentence embedding framework to collaborate the current sentence representation, word-based content and topic assignment of the sentence to predict the next sentence representation. The experiments on summarization tasks show our model outperforms state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85035043594&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85035043594
SN - 1613-0073
VL - 1986
SP - 21
EP - 26
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2017 IJCAI Workshop on Semantic Machine Learning, SML 2017
Y2 - 20 August 2017
ER -