Semantic title evaluation and recommendation based on topic models

Huidong Jin, Lijiu Zhang, Lan Du

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


To digest tremendous documents efficiently, people often resort to their titles, which normally provide a concise and semantic representation of main text. Some titles however are misleading due to lexical ambiguity or eye-catching intention. The requirement of reference summaries hampers using traditional lexical summarisation evaluation techniques for title evaluation. In this paper we develop semantic title evaluation techniques by comparing a title with other sentences in terms of topic-based similarity with regard to the whole document. We further give a statistical hypothesis test to check whether a title is favourable without any reference summary. As a byproduct, the top similar sentence can be recommended as a candidate for title. Experiments on patents, scientific papers and DUC'04 benchmarks show our Semantic Title Evaluation and Recommendation technique based on a recent Segmented Topic Model (STERSTM), performs substantially better than that based on the canonical model Latent Dirichlet Allocation (STERLDA). It can also recommend titles with quality comparable with the winners of DUC'04 in terms of summarising documents into very short summaries.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
EditorsJian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu
Number of pages12
Volume7819 LNAI
EditionPART 2
Publication statusPublished - 2013
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia
Duration: 14 Apr 201317 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7819 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Other17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
CityGold Coast, QLD


  • Evaluation
  • Hypothesis test
  • Semantic
  • Topic models

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