Effective Tweet Contextualization with Hashtags Performance Prediction and Multi-Document Summarization

Romain Deveaud (1) , Florian Boudin (2)

1 LIA - Laboratoire Informatique d'Avignon
2 LS2N - équipe TALN - Traitement Automatique du Langage Naturel

In this paper we describe our participation in the INEX 2013 Tweet Contextualization track and present our contributions. Our ap- proach is the same as last year, and is composed of three main com- ponents: preprocessing, Wikipedia articles retrieval and multi-document summarization. We however took advantage of a larger use of hashtags in the topics and used them to enhance the retrieval of relevant Wikipedia articles. We also took advantage of the training examples from last year which allowed us to learn the weights of each sentence selection feature. Two of our submitted runs achieved the two best informativeness results, while our generated contexts where almost as readable as those of the most readable system.

Domaines: Traitement du texte et du document

Romain Deveaud, Florian Boudin. Effective Tweet Contextualization with Hashtags Performance Prediction and Multi-Document Summarization. INitiative for the Evaluation of XML Retrieval (INEX), Dec 2013, Valence, Spain. pp.n/a. ⟨hal-00917956⟩