Overview of INEX Tweet Contextualization 2013 track

Abstract

Twitter is increasingly used for on-line client and audience fishing; this motivated the tweet contextualization task at INEX. The objective is to help a user to understand a tweet by providing him with a short summary (500 words). This summary should be built automatically using local resources like the Wikipedia and generated by extracting relevant passages and aggregating them into a coherent summary. The task is evaluated considering informativeness which is computed using a variant of Kullback-Leibler divergence and passage pooling. Meanwhile effective readability in context of summaries is checked using binary questionnaires on small samples of results. Running since 2010, results show that only systems that efficiently combine passage retrieval, sentence segmentation and scoring, named entity recognition, text POS analysis, anaphora detection, diversity content measure as well as sentence reordering are effective.