A solution to extractive summarization based on document type and a new measure for sentence similarity - MELLO, Rafael Ferreira Leite de (2015)
The Internet is a enormous and fast growing digital repository encompassing billions of documents in a diversity of subjects, quality, reliability, etc. It is increasingly difficult to scavenge useful information from it. Thus, it is necessary to provide automatically techniques that allowing users to save time and resources. Automatic text summarization techniques may offer a way out to this problem. Text summarization (TS) aims at automatically compress one or more documents to present their main ideas in less space. TS platforms receive one or more documents as input to generate a summary. In recent years, a variety of text summarization methods has been proposed. However, due to the different document types (such as news, blogs, and scientific articles) it became difficult to create a general TS application to create expressive summaries for each type. Another related relevant problem is measuring the degree of similarity between sentences, which is used in applications, such as: text summarization, information retrieval, image retrieval, text categorization, and machine translation. Recent works report several efforts to evaluate sentence similarity by representing sentences using vectors of bag of words or a tree of the syntactic information among words. However, most of these approaches do not take in consideration the sentence meaning and the words order. This thesis proposes: (i) a new text summarization solution which identifies the document type before perform the summarization, (ii) the creation of a new sentence similarity measure based on lexical, syntactic and semantic evaluation to deal with meaning and word order problems. The previous identification of the document types allows the summarization solution to select the methods that is more suitable to each type of text. This thesis also perform a detailed assessment with the most used text summarization methods to selects which create more informative summaries for news, blogs and scientific articles contexts.The sentence similarity measure proposed is completely unsupervised and reaches results similar to humans annotator using the dataset proposed by Li et al. The proposed measure was satisfactorily applied to evaluate the similarity between summaries and to eliminate redundancy in multi-document summarization.