A Method for Short Message Contextualization: Experiments at CLEF/INEX

Liana Ermakova (1, 2)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 Perm State University

This paper presents the approach we developed for automatic multi-document summarization applied to short message contextualization, in particular to tweet contextualization. The proposed method is based on named entity recognition, part-of-speech weighting and sentence quality measuring. In contrast to previous research, we introduced an algorithm from smoothing from the local context. Our approach exploits topic-comment structure of a text. Moreover, we developed a graph-based algorithm for sentence reordering. The method has been evaluated at INEX/CLEF tweet contextualization track. We provide the evaluation results over the 4 years of the track. The method was also adapted to snippet retrieval and query expansion. The evaluation results indicate good performance of the approach.

Mots clés: Information retrieval; Tweet contextualization; Summarization; Snippet; Sentence extraction; Readability; Topic-comment structure

Domaines: Théorie de l'information [cs.IT]; Recherche d'information [cs.IR]

Liana Ermakova. A Method for Short Message Contextualization: Experiments at CLEF/INEX. 6th Conference on Multilingual and Multimodal Information Access Evaluation (CLEF 15), Sep 2015, Toulouse, France. pp. 352-363. ⟨hal-01343032⟩