TY - GEN
T1 - Aligning gaussian-topic with embedding network for summarization ranking
AU - Wei, Linjing
AU - Huang, Heyan
AU - Gao, Yang
AU - Wei, Xiaochi
AU - Feng, Chong
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Query-oriented summarization addresses the problem of information overload and help people get the main ideas within a short time. Summaries are composed by sentences. So, the basic idea of composing a salient summary is to construct quality sentences both for user specific queries and multiple documents. Sentence embedding has been shown effective in summarization tasks. However, these methods lack of the latent topic structure of contents. Hence, the summary lies only on vector space can hardly capture multi-topical content. In this paper, our proposed model incorporates the topical aspects and continuous vector representations, which jointly learns semantic rich representations encoded by vectors. Then, leveraged by topic filtering and embedding ranking model, the summarization can select desirable salient sentences. Experiments demonstrate outstanding performance of our proposed model from the perspectives of prominent topics and semantic coherence.
AB - Query-oriented summarization addresses the problem of information overload and help people get the main ideas within a short time. Summaries are composed by sentences. So, the basic idea of composing a salient summary is to construct quality sentences both for user specific queries and multiple documents. Sentence embedding has been shown effective in summarization tasks. However, these methods lack of the latent topic structure of contents. Hence, the summary lies only on vector space can hardly capture multi-topical content. In this paper, our proposed model incorporates the topical aspects and continuous vector representations, which jointly learns semantic rich representations encoded by vectors. Then, leveraged by topic filtering and embedding ranking model, the summarization can select desirable salient sentences. Experiments demonstrate outstanding performance of our proposed model from the perspectives of prominent topics and semantic coherence.
KW - Embedding model
KW - Query-oriented summarization
KW - Topic
UR - http://www.scopus.com/inward/record.url?scp=85028468264&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-63579-8_46
DO - 10.1007/978-3-319-63579-8_46
M3 - Conference contribution
AN - SCOPUS:85028468264
SN - 9783319635781
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 610
EP - 625
BT - Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings
A2 - Shahabi, Cyrus
A2 - Lian, Xiang
A2 - Jensen, Christian S.
A2 - Yang, Xiaochun
A2 - Chen, Lei
PB - Springer Verlag
T2 - 1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017
Y2 - 7 July 2017 through 9 July 2017
ER -