Keyphrase Extraction of News Web Pages

Abstract

Keyphrase extraction from news web pages is an important task for news documents retrieval and summarization. Keyphrases are like index terms that enclose the important information about document content. Keyphrases actually offer concise and precise description of document content. Key phrases are considered as a single word or a combination of more than one word that represent the important concepts in a text documents. The aim of this paper is to develop and evaluate an automatic keyphrases extraction approach for news web pages. Our approach identifies the candidate keyphrases from documents and chooses those candidate keyphrase having highest weight score. Weight formula combines the feature set that includes TF*IDF, phrase disatnce in documents and lexical chain that is based on WordNet to represent semantic relations between words. The experimental results show that the performance of our approach is better than the contemporary approaches today.

Multi-Document News Web Page Summarization Using Content Extraction and Lexical Chain Based Key Phrase Extraction

“…The proposed methodology has been to find the set of keyphrases that signify the main units of data and find the best set of sentences that cover more relevant information. Here, the keyphrases are extracted using the lexical chain as we discussed in our previous paper [39]. According to our previous work, lexical chain is created by taking a new phrase and finding a related chain for it according to lexical cohesion.…”

“…ROUGE-2 = 11/13 = 0.846 = 84.6% ROUGE-SU4 is considered as a comprehensive version of ROUGE-2 that allows maximum 4-length word-level gaps between the bigram [39].…”