Using semantic folding with TextRank for automatic summarization

Abstract: This master thesis deals with automatic summarization of text and how semantic folding can be used as a similarity measure between sentences in the TextRank algorithm. The method was implemented and compared with two common similarity measures. These two similarity measures were cosine similarity of tf-idf vectors and the number of overlapping terms in two sentences. The three methods were implemented and the linguistic features used in the construction were stop words, part-of-speech filtering and stemming. Five different part-of-speech filters were used, with different mixtures of nouns, verbs, and adjectives. The three methods were evaluated by summarizing documents from the Document Understanding Conference and comparing them to gold-standard summarization created by human judges. Comparison between the system summaries and gold-standard summaries was made with the ROUGE-1 measure. The algorithm with semantic folding performed worst of the three methods, but only 0.0096 worse in F-score than cosine similarity of tf-idf vectors that performed best. For semantic folding, the average precision was 46.2% and recall 45.7% for the best-performing part-of-speech filter.