Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering

By Hongyuan Zha. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

343 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/42e0376c29ad9510464b7a643a49cfc3b60c2cad

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Citations in the corpus

Automatic Keyphrase Extraction: A Survey of the State of the Art (2014, background): “Zha (2002) proposes the ﬁrst graph-based approach for simultaneous summarization and keyphrase extraction, motivated by a key observation: a sentence is important if it contains important words, and important words appear in important sentences.”
Recent automatic text summarization techniques: a survey (2016, unknown): “Therefore, LSA based summarization methods are unable to select meaningful sentences (Zha 2002; Lee and Seung 1999).”
Cross-Language Document Summarization Based on Machine Translation Quality Prediction (2010, methodology): “Other methods include mutual reinforcement principle (Zha 2002; Wan et al., 2007).”
LexRank: Graph-based Centrality as Salience in Text Summarization (2004, background): “Zha (2002) defines a bipartite graph from the set of terms to the set of sentences.”
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization (2008, methodology): “Current document summarization methods usually involve natural language processing and machine learning techniques [29, 2, 34], such as classi(cid:12)cation, clustering, conditional random (cid:12)elds (CRF), etc. Section 2 will explicitly discuss these existing methods.”
Improving the Estimation of Word Importance for News Multi-Document Summarization (2014, background): “A handful of investigations have productively explored the mutually reinforcing relationship between word and sentence importance, iteratively re-estimating each in either supervised or unsupervised framework (Zha, 2002; Wan et al., 2007; Wei et al., 2008; Liu et al., 2011).”
Automatic generic document summarization based on non-negative matrix factorization (2009, methodology): “As a result, LSA-related methods of document summarization may fail to extract meaningful sentences (Lee & Seung, 1999; Zha, 2002).”
Self reinforcement for important passage retrieval (2013, background): “Closely related to our work are the unsupervised key phrase extraction approaches that have been explored to reinforce summarization [32, 29, 11, 22, 24].”
PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents (2017, methodology): “In addition, keyphrases are shown to be rich sources of information in many natural language processing and information retrieval tasks such as scientific paper summarization, classification, recommendation, clustering, and search (Abu-Jbara and Radev, 2011; Qazvinian et al., 2010; Jones and Stavele”
Learning to recognize reliable users and content in social media with coupled mutual reinforcement (2009, methodology): “Building on the previous work, our framework is based on the model of network relationships in social media [13] and exploits the mutual reinforcement principle [16].”
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization (2004, background): “Zha (2002) denes a bipartite graph from the set of terms to the set of sentences.”