From Phrases to Keyphrases

Abstract

Automatic keyphrase extraction aims at extracting a compact representation of a single document which can be used for various applications such as indexing, classification or summarization. Existing methods for keyphrase extraction usually define the set of phrases of a document as a crisp set and by scoring the phrases, they select the keyphrases of the document. In this work we define the set of phrases inside a document to be a fuzzy set, and based on the membership values of the phrases, we select the ones with higher membership values as the keyphrases of the document. Moreover we propose a novel evaluation method inspired by the Turing test which can be used for extractive summarization tasks.

Podcast Accessibility Through Transcription and Summarization With Speech Synthesis

“…Hannah et al [17] developed a fuzzy inference system to assess sentence scores based on various features, and the rules are activated based on the overall score of individual sentences to generate the summary. Modaresi and Conrad [18] defined a set of phrases as a fuzzy set and utilized the membership degrees of phrases. High membership values were regarded as key phrases using the maximum entropy model.…”