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Teachers and professors have increasingly employed different methods to enrich the learning of a subject in class, drive other assignments, and meet curriculum standards, are some examples of it. One of such methods is the use of movies as an alternative educational experience to support class discussions. In this sense, websites such as www.TeachWithMovies.com, arise as a valuable support to the creation of lesson plans. In this website, each movie is described as a lesson plan targeting the learn of a subject, the plans comprise the movie benefits and possible problems, a helpful background, the discussion and some interesting questions to be presented to the students. However, the creation of such lesson plan or even a simple educational description of the movie can demand much work and time, since the developed text must consider educational aspects of the movie. In this paper, we describe BEATnIk (Biased Educational Automatic Text Summarization) an algorithm to automatic generation of movie’s summaries, such algorithm favors educational aspects from the text to generate a biased educational summary. The algorithm input, are users’ comments about the each movie that has a lesson plan in the TeachWithMovies (TWM) website, and the user’s comments extracted from amazon’s website. BEATnIk constructs a complete graph for each movie where each sentence from the comments’ set becomes a node, and each edge weight is defined by the value of an adapted cosine similarity between the sentences. The algorithm then employs the PageRank to compute the centrality of each node. The intuition behind this approach is that central sentences highlight aspects of a movie that many other reviews frequently mention. In addition, BEATnIk takes into account keywords extracted from the lesson plans of TWM. The final educational summary is based on the centrality score of the sentences pondered by the presence of educational keywords. The comparison of our approach to TextRank, which is a Graph-based Automatic Text Summarization, revealed that BEATnIk generate summaries closer to the description of the movies in TWM. The experiments showed that our approach statistically outperforms the baseline in precision, and achieves better results both in recall and f-score using ROUGE-n, which is a set of metrics used for evaluating automatic summarization.
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