DegExt: a language-independent keyphrase extractor
In this paper, we introduce DegExt, a graph-based language-independent keyphrase extractor, which extends the keyword extraction method described in Litvak and Last (Graph-based keyword extraction for single-document summarization. In: Proceedings of the workshop on multi-source multilingual information extraction and summarization, pp 1724, 2008). We compare DegExt with two state-of-the-art approaches to keyphrase extraction: GenEx (Turney in Inf Retr 2:303336, 2000) and TextRank (Mihalcea and Tarau in Textrankbringing order into texts. In: Proceedings of the conference on empirical methods in natural language processing. Barcelona, Spain, 2004). We evaluated DegExt on collections of benchmark summaries in two different languages: English and Hebrew. Our experiments on the English corpus show that DegExt significantly outperforms TextRank and GenEx in terms of precision and area under curve for summaries of 15 keyphrases or more at the expense of a mostly non-significant decrease in recall and F-measure, when the extracted phrases are matched against gold standard collection. Due to DegExts tendency to extract bigger phrases than GenEx and TextRank, when the single extracted words are considered, DegExt outperforms them both in terms of recall and F-measure. In the Hebrew corpus, DegExt performs the same as TextRank disregarding the number of keyphrases. An additional experiment shows that DegExt applied to the TextRank representation graphs outperforms the other systems in the text classification task. For documents in both languages, DegExt surpasses both GenEx and TextRank in terms of implementation simplicity and computational complexity.
keyphrase extraction summarization text mining graph-based document representation node centrality