KEST: A graph-based keyphrase extraction technique for tweets summarization using Markov Decision Process. (15th December 2022)

• Record Type: Journal Article
• Authors: Garg, Muskan; Kumar, Mukesh
• Is Part Of: Expert systems with applications. Volume 209(2022)
• Publication Date: 2022-12-15
• Subjects: Graph of words -- Interest -specific keyphrases -- Markov decision process -- Tweet summarization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118110
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Multi-document summarization finds its application in many downstream information retrieval and natural language processing tasks. In the light of recent developments in social media data mining, Tweet summarization has emerged as a fundamental task of automatically detecting important keyphrases from a set of Tweets about current happenings. In the existing literature, the graph-based keyphrase extraction techniques are well-established unsupervised algorithms to capture summaries from dynamically evolving data. We argue that the traditional multi-tweet summarization technique may or may not capture user's interest-specific keyphrases during tweet summarization. The nature of user-generated factual short-text is different from well-formed descriptive and perceptual long-text due to their repetitive nature. In this context, we introduce a simple yet effective interest-specific keyphrase extraction technique for tweet summarization as KEST: K ey E xtraction for S ummarization of T weets using Markov Decision Process (MDP). In this research work, we generate a path as evolving chain of highly interconnected words from sub-components in graph of words. We evaluate the effectiveness of our computationally, inexpensive, graph-based, abstractive keyphrase extraction approach over two datasets which we make publicly available. Highlights: The patterns for word co-occurrence network evolved from social media data. The Markov Decision Process over Graph of Words. Propose transition and controlling parameters to approximate chain of words. Keyphrase extraction by retaining lexical sequence of words.