Video highlights detection and summarization with lag-calibration based on concept-emotion mapping of crowd-sourced time-sync comments

Qing Ping, Chaomei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept- mapped lexical-chains for lagcalibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping. Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.

Original languageEnglish
Title of host publicationEMNLP 2017 - Workshop on New Frontiers in Summarization, NFiS 2017 - Workshop Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages1-14
Number of pages14
ISBN (Electronic)9781945626890
Publication statusPublished - 2017
EventEMNLP 2017 Workshop on New Frontiers in Summarization, NFiS 2017 - Copenhagen, Denmark
Duration: 2017 Sep 7 → …

Publication series

NameEMNLP 2017 - Workshop on New Frontiers in Summarization, NFiS 2017 - Workshop Proceedings

Conference

ConferenceEMNLP 2017 Workshop on New Frontiers in Summarization, NFiS 2017
Country/TerritoryDenmark
CityCopenhagen
Period17/9/7 → …

Bibliographical note

Publisher Copyright:
© EMNLP 2017.All right reserved.

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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