Bibtex de la publication

@InProceedings{ Mo2016.15,
author = {Moulahi, Bilel and Ben Jabeur, Lamjed and Chellal, Abdelhamid and Palmer, Thomas and Tamine, Lynda and Boughanem, Mohand and Pinel-Sauvagnat, Karen and Hubert, Gilles},
title = "{IRIT at TREC Real Time Summarization 2016 (regular paper)}",
booktitle = "{Text REtrieval Conference (TREC), Gaithersburg, Maryland USA, 15/11/2016-18/11/2016}",
year = {2016},
month = {novembre},
publisher = {National Institute of Standards and Technology (NIST)},
address = {http://www-nlpir.nist.gov/},
pages = {(on line)},
language = {anglais},
URL = {http://trec.nist.gov/pubs/trec25/papers/IRIT-RT.pdf - http://oatao.univ-toulouse.fr/18823/},
keywords = {real-time, social media, user profile, word similarity, filtering, clustering, rapidity, entities, personalization},
abstract = {This paper presents the participation of the IRIT laboratory (University of Toulouse) to the Real Time Summarization track of TREC 2016. This track consists in a real-time filtering the tweet stream and identifying both relevant and novel tweets to be pushed to user in real-time. Our team proposes three different approaches: (1) The first approach consist of a filtering model that combines several summarization constraints (2) The second approach for the scenario A is composed of three filters adjusted sequentially in which we use word similarity based function to evaluate the relevance of an incoming tweet. The generation of a batch of up to 100 ranked tweets is formulate as an optimization problem. (3) The third approach consist of a step by step stream selection method focusing on rapidity, and taking into account tweet similarity as well as several features including content, entities and user-related aspects. We describe in this paper the three proposed approaches and we discuss official obtained results for each of them.}
}