On the evaluation of tweet timeline generation task

Walid Magdy, Tamer Elsayed, Maram Hasanain

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

2 Citations (Scopus)

Abstract

Tweet Timeline Generation (TTG) task aims to generate a timeline of relevant but novel tweets that summarizes the development of a given topic. A typical TTG system first retrieves tweets then detects novel tweets among them to form a timeline. In this paper, we examine the dependency of TTG on retrieval quality, and its effect on having biased evaluation. Our study showed a considerable dependency, however, ranking systems is not highly affected if a common retrieval run is used.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Proceedings
EditorsMarie-Francine Moens, Nicola Ferro, Gianmaria Silvello, Giorgio Maria di Nunzio, Claudia Hauff, Fabio Crestani, Josiane Mothe, Fabrizio Silvestri
PublisherSpringer Verlag
Pages648-653
Number of pages6
ISBN (Print)9783319306704
DOIs
Publication statusPublished - 1 Jan 2016
Event38th European Conference on Information Retrieval Research, ECIR 2016 - Padua, Italy
Duration: 20 Mar 201623 Mar 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9626
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other38th European Conference on Information Retrieval Research, ECIR 2016
CountryItaly
CityPadua
Period20/3/1623/3/16

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Magdy, W., Elsayed, T., & Hasanain, M. (2016). On the evaluation of tweet timeline generation task. In M-F. Moens, N. Ferro, G. Silvello, G. M. di Nunzio, C. Hauff, F. Crestani, J. Mothe, & F. Silvestri (Eds.), Advances in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Proceedings (pp. 648-653). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9626). Springer Verlag. https://doi.org/10.1007/978-3-319-30671-1_48