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Standard informativeness measures used to evaluate Automatic TextSummarization mostly rely on n-gram overlapping between the automatic summaryand the reference summaries. These measures differ from the metric they use(cosine, ROUGE, Kullback-Leibler, Logarithm Similarity, etc.) and the bag ofterms they consider (single words, word n-grams, entities, nuggets, etc.).Recent word embedding approaches offer a continuous alternative to discreteapproaches based on the presence/absence of a text unit. Informativenessmeasures have been extended to Focus Information Retrieval evaluation involvinga user's information need represented by short queries. In particular for thetask of CLEF-INEX Tweet Contextualization, tweet contents have been consideredas queries. In this paper we define the concept of Interestingness as ageneralization of Informativeness, whereby the information need is diverse andformalized as an unknown set of implicit queries. We then study the ability ofstate of the art Informativeness measures to cope with this generalization.Lately we show that with this new framework, standard word embeddingsoutperforms discrete measures only on uni-grams, however bi-grams seems to be akey point of interestingness evaluation. Lastly we prove that the CLEF-INEXTweet Contextualization 2012 Logarithm Similarity measure provides bestresults.

Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)