TextRank

Keyphrases are widely used as a brief summary of documents. Since man- ual assignment is time-consuming, vari- ous unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. …...

. In this paper, we discuss the summarization for supporting a user’s judgment on the credibility of information on the Web. In general, if a statement contradicts another statement, the …...

Automatic keyphrase extraction methods have generally taken either supervised or unsupervised approaches. Supervised methods extract keyphrases by using a training document set, thus acquiring knowledge from a global collection of …...

. We report a series of experiments with different semantic models on top of various statistical models for extractive text summarization. Though statistical models may better cap- ture word co-occurrences …...

AbstractWe introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to …...

AbstractPretrained transformer-based models such as BERT have demonstrated state-of-the-art predictive performance when adapted into a range of natural language processing tasks. An open problem is how to improve the faithfulness …...

AbstractManifold ranking has been successfully applied in query-oriented multi-document summariza-tion. It not only makes use of the relationships among the sentences but also the relationships between the given query and …...

AbstractWith the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information. The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task. …...