SASI: sumarizador automático de documentos baseado no problema do subconjunto independente de vértices

XLVI Simpósio Brasileiro de Pesquisa OperacionalThis article discusses a summarizer system of documents named SASI. This system features an innovative approach to provide automatic summaries, based on the determination of the maximum independent subset of vertices, modeling the problem a graph of phrases (vertices) and the relationships between them (edges). The concepts and operation of the proposed summarizer and a series of tests comparing the results provided by SASI with others summarizer systems are described. Initial results are promising, evaluating questions of informativeness of the produced summaries on the parameters of time and algorithmic complexity

INEX Tweet Contextualization Task: Evaluation, Results and Lesson Learned

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