INEX Tweet Contextualization task: Evaluation, results and lesson learned
A full summary report on the four year long Tweet Contextualization task.A detail on evaluation metrics and framework we developed for tweet contextualization evaluation.A deep analysis of what the participants suggested in their approaches by categorizing the various methods.A description of the data made available to the community. Microblogging platforms such as Twitter are increasingly used for on line client and market analysis. This motivated the proposal of a new track at CLEF INEX lab of Tweet Contextualization. The objective of this task was to help a user to understand a tweet by providing him with a short explanatory summary (500 words) This summary should be built automatically using resources like Wikipedia and generated by extracting relevant passages and aggregating them into a coherent summary.Running for four years, results show that the best systems combine NLP techniques with more traditional methods. More precisely the best performing systems combine passage retrieval, sentence segmentation and scoring, named entity recognition, text part of speech (POS) analysis, anaphora detection, diversity content measure as well as sentence reordering.This paper provides a full summary report on the four year long task. While yearly overviews focused on system results, in this paper we provide a detailed report on the approaches proposed by the participants and which can be considered as the state of the art for this task. As an important result from the 4 years competition, we also describe the open access resources that have been built and collected. The evaluation measures for automatic summarization designed in DUC or MUC were not appropriate to evaluate tweet contextualization, we explain why and depict in detailed the LogSim measure used to evaluate informativeness of produced contexts or summaries. Finally, we also mention the lessons we learned and that it is worth considering when designing a task.
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