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Graph-Based Multi-Modality Learning for Topic-Focused Multi-Document Summarization

Xiaojun Wan, Jianguo Xiao
2009 International Joint Conference on Artificial Intelligence  
Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization.  ...  This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and  ...  Introduction Topic-focused (or query-based) multi-document summarization aims to create from a document set a summary which answers the need for information expressed in a given topic or query.  ... 
dblp:conf/ijcai/WanX09 fatcat:vbqtjwqibfbpfaywddlxztr3ci

Topic analysis for topic-focused multi-document summarization

Xiaojun Wan
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
Topic-focused multi-document summarization aims to produce a summary biased to a given topic or user profile.  ...  This paper presents a novel extractive approach based on manifold-ranking of sentences to this summarization task.  ...  Given a specified topic description (i.e. user profile, user query), topic-focused multi-document summarization (i.e. query-based multi-document summarization) is to create from the documents a summary  ... 
doi:10.1145/1645953.1646184 dblp:conf/cikm/Wan09 fatcat:hgsvn4lsajht5l5k74mfo3jgdq

IBM in TREC 2006 Enterprise Track

Jennifer Chu-Carroll, Guillermo A. Averboch, Pablo Ariel Duboué, David Gondek, J. William Murdock, John M. Prager, Paul Hoffmann, Janyce Wiebe
2006 Text Retrieval Conference  
MEAD 7 [Radev et al., 2003], a query-based multi-document summarizer was used to generate the pseudo-document for each candidate.  ...  Finally, the summarization approach experiments with the utility of a multi-document summarization system for capturing the expertise of a given person.  ...  Our discussion task results show that our conservative query expansion strategy focusing on FOLDOC-identified domain-specific terms improves performance slightly, while combining the results from multiple  ... 
dblp:conf/trec/Chu-CarrollADGMPHW06 fatcat:pcnwejvgcrdq5h523g3sge7oiu

Generating Query Focused Summaries from Query-Free Resources [article]

Yumo Xu, Mirella Lapata
2021 arXiv   pre-print
In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available.  ...  We introduce MaRGE, a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be  ...  We have |D| = 1 for single-document summarization (SDS) and |D| > 1 for multi-document summarization (MDS).  ... 
arXiv:2012.14774v2 fatcat:wbvtgpome5eyjcp6y5zrrh7t74

HAR: Hub, Authority and Relevance Scores in Multi-Relational Data for Query Search [chapter]

Xutao Li, Michael K. Ng, Yunming Ye
2012 Proceedings of the 2012 SIAM International Conference on Data Mining  
In this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search.  ...  In the comparison, we find that the performance of HAR is better than those of HITS, SALSA and TOPHITS.  ...  Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization. 1 1 Evolutionary document summarization for disaster management. 1 1 A matrix density based algorithm  ... 
doi:10.1137/1.9781611972825.13 dblp:conf/sdm/LiNY12 fatcat:kadu6nbcbngcfgvdzvhulrk7l4

Query-focused Multi-document Summarization: Combining a Novel Topic Model with Graph-based Semi-supervised Learning [article]

Jiwei Li, Sujian Li
2013 arXiv   pre-print
Graph-based semi-supervised learning has proven to be an effective approach for query-focused multi-document summarization.  ...  The problem of previous semi-supervised learning is that sentences are ranked without considering the higher level information beyond sentence level.  ...  for query-focused multi-document summarization.  ... 
arXiv:1212.2036v3 fatcat:sigpxdsgybhfva3wjzvnufxas4

A Survey of Unstructured Text Summarization Techniques

Sherif Elfayoumy, Jenny Thoppil
2014 International Journal of Advanced Computer Science and Applications  
and make decisions based on document contents.  ...  By improving summarizing techniques, precision of document retrieval through search queries against summarized documents is expected to improve in comparison to querying against the full spectrum of original  ...  Maximal Marginal Relevance (MMR) MMR is based on the vector space model of text retrieval [15] [17] and is well suited for query-based and multi-document summarization.  ... 
doi:10.14569/ijacsa.2014.050421 fatcat:dh6mh2brabbw7eok465x46a7fa

Supervised Lazy Random Walk for Topic-Focused Multi-document Summarization

Pan Du, Jiafeng Guo, Xueqi Cheng
2011 2011 IEEE 11th International Conference on Data Mining  
Topic-focused multi-document summarization aims to produce a summary given a specific topic description and a set of related documents.  ...  Moreover, our approach can achieve the three major goals of topic-focused multi-document summarization (i.e. relevance, salience and diversity) simultaneously with a unified ranking process.  ...  Zha [20] proposed a mutual reinforcement principle for sentence extraction using HITS. Wan et al. [3] applied a manifold-ranking algorithm to query-focused summarization.  ... 
doi:10.1109/icdm.2011.140 dblp:conf/icdm/DuGC11 fatcat:mtebz3hhl5bx7m5k7gr3dkku74

IIIT Hyderabad in Summarization and Knowledge Base Population at TAC 2011

Vasudeva Varma, Sudheer Kovelamudi, Arpit Sood, Jayant Gupta, Harshit Jain, Pattisapu Nikhil Priyatam, Aditya Mogadala, Srikanth Reddy Vaddepally
2011 Text Analysis Conference  
For multilingual summarization task, we investigated the HAL ( Hyperspace Analogue to Language Model) where we created a semantic space from word co-occurrences.  ...  Wikipedia based extraction methods and topic modelling are used to score sentences in guided summarization track.  ...  Scores are generated using the existing weighted graph-based ranking algorithms given below. • Hyperlinked Induced Topic Search (HITS): HIT S A (V i ) = V j In(V i )HIT S H (V j ) HIT S H (V i ) = V j  ... 
dblp:conf/tac/VarmaKSGJPMR11 fatcat:yl6gzgxnk5g2bo7wknu7msjpuq

Exploiting relevance, coverage, and novelty for query-focused multi-document summarization

Wenjuan Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi
2013 Knowledge-Based Systems  
Specifically, for query-focused summarization, there exist three challenges: (1) how to retrieve query relevant sentences; (2) how to concisely cover the main aspects (i.e., topics) in the document; and  ...  Summarization plays an increasingly important role with the exponential document growth on the Web.  ...  In this paper, we focus on unsupervised, extract-based, query-focused, multi-document summarization.  ... 
doi:10.1016/j.knosys.2013.02.015 fatcat:wqsgiyiugrghhb3vadmolxdp7a

A Document Clustering and Ranking System for Exploring MEDLINE Citations

Y. Lin, W. Li, K. Chen, Y. Liu
2007 JAMIA Journal of the American Medical Informatics Association  
Design: A text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries.  ...  Conclusions: The text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups. Ⅲ J Am Med  ...  In this study, the sentences derived from MEAD, a multi-document summarizer, were not informative compared to the keywords generated by HITS.  ... 
doi:10.1197/jamia.m2215 pmid:17600104 pmcid:PMC1975797 fatcat:zw5l34xjz5fajkgqfhxdiuzgqm

Extractive Text Summarization Using Recent Approaches: A Survey

Avaneesh Kumar Yadav, Ashish Kumar Maurya, Ranvijay, Rama Shankar Yadav
2021 Ingénierie des Systèmes d'Information  
Many authors proposed various techniques for both types of text summarization. This paper presents a survey of extractive text summarization on graphical-based techniques.  ...  In extractive text summarization, a summary is created from the given document that contains crucial sentences of the document.  ...  In query-based summarization, a summary is generated by user queries. This summarization is also called user-focused or topic-focused summarization [10] .  ... 
doi:10.18280/isi.260112 fatcat:zwo7neckujanliou7arlaiccj4

A Novel Relational Learning-to-Rank Approach for Topic-Focused Multi-document Summarization

Yadong Zhu, Yanyan Lan, Jiafeng Guo, Pan Du, Xueqi Cheng
2013 2013 IEEE 13th International Conference on Data Mining  
Topic-focused multi-document summarization aims to produce a summary over a set of documents and conveys the most important aspects of a given topic.  ...  Specifically, the ranking function is defined as the combination of content-based score of individual sentence, and relation-based score between the current sentence and those already selected.  ...  OUR APPROACH As described in Section 2, many traditional methods formalize the topic-focused multi-document summarization as a ranking problem.  ... 
doi:10.1109/icdm.2013.38 dblp:conf/icdm/ZhuLGDC13 fatcat:hpv7jepkxzcgnozqewie2j4twu

Multi-document summarization using cluster-based link analysis

Xiaojun Wan, Jianwu Yang
2008 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08  
The Markov Random Walk model has been recently exploited for multi-document summarization by making use of the link relationships between sentences in the document set, under the assumption that all the  ...  This paper proposes the Cluster-based Conditional Markov Random Walk Model (ClusterCMRW) and the Cluster-based HITS Model (ClusterHITS) to fully leverage the cluster-level information.  ...  Other related work includes topic-focused document summarization [3] , which aims to produce summary biased to a given topic or query.  ... 
doi:10.1145/1390334.1390386 dblp:conf/sigir/WanY08 fatcat:djxvs7yrbbcoxglefqdbv7ukui

Automatic Keyword Extraction for Text Summarization: A Survey [article]

Santosh Kumar Bharti, Korra Sathya Babu
2017 arXiv   pre-print
Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in original document without losing any critical purposes.  ...  In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction.  ...  It exploits the property of sentence ranking methods in which they consider neural query ranking and query-focused ranking. Dong et al.  ... 
arXiv:1704.03242v1 fatcat:poa2yh2uhbcgfaemgfqa5ylxim
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