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Summarization Approaches Based on Document Probability Distributions

Sandeep Sripada, Jagadeesh Jagarlamudi
2009 Pacific Asia Conference on Language, Information and Computation  
In this paper, we also discuss two summary generation approaches that were designed based on the above hypothesis (a) Summary generation by extraction of sentences based on its coverage and (b) Minimum  ...  We assume that a summary would be able to replace or act as a substitute for the document if its probability distribution is similar to that of the original document.  ...  the probability distributions.  ... 
dblp:conf/paclic/SripadaJ09 fatcat:i25mqblhyzhhpedcany5fjkwze

Topic-sensitive multi-document summarization algorithm

Liu Na, Tang Di, Lu Ying, Tang Xiao-Jun, Wang Hai-Wen
2015 Computer Science and Information Systems  
This paper proposed a topic-sensitive algorithm for multi-document summarization.  ...  The experiments showed that the proposed algorithm achieves better performance than the other state-of-the-art algorithms on DUC2002 corpus.  ...  Zhou S proposed an automatic summarization algorithm based on topic distribution and words distribution in 2014.  ... 
doi:10.2298/csis140815060n fatcat:qkeaeycbd5cy7mdmiikwmzyd5y

A Probabilistic Generative Framework for Extractive Broadcast News Speech Summarization

Yi-Ting Chen, B. Chen, Hsin-Min Wang
2009 IEEE Transactions on Audio, Speech, and Language Processing  
Each sentence of a spoken document to be summarized was treated as a probabilistic generative model for predicting the document.  ...  Index Terms: spoken document summarization, hidden Markov model, relevance model, word topical mixture model, whole sentence maximum entropy model  ...  In general, the approaches can fall into three main categories: 1) approaches based on the sentence structure or location information, 2) approaches based on statistical measures, and 3) approaches based  ... 
doi:10.1109/tasl.2008.2005031 fatcat:6x5c42vw4fdsdjb6kognhudo5y

Different approaches for identifying important concepts in probabilistic biomedical text summarization [article]

Milad Moradi, Nasser Ghadiri
2017 arXiv   pre-print
The results show that the Bayesian summarizer outperforms the biomedical summarizers that rely on the frequency of concepts, the domain-independent and baseline methods based on the Recall-Oriented Understudy  ...  Some of the biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text.  ...  In this paper, we address these questions, describing a Bayesian summarization method based on the probability distribution of concepts within the input document.  ... 
arXiv:1605.02948v3 fatcat:46op7elaprettdnwkz3y4wdw4m

Estimating Risk of Picking a Sentence for Document Summarization [chapter]

Chandan Kumar, Prasad Pingali, Vasudeva Varma
2009 Lecture Notes in Computer Science  
We calculate the risk of information loss associated with each sentence and extract sentences based on ascending order of their risk.  ...  The primary task of document summarization process is to pick subset of sentences as a representative of whole document set.  ...  Relative entropy is a loss function between two probability distributions which measures how bad a given probability distribution is in modeling the other one.  ... 
doi:10.1007/978-3-642-00382-0_46 fatcat:e7ytco6mg5hp3plgpldnuaa4j4

Extractive spoken document summarization for information retrieval

Berlin Chen, Yi-Ting Chen
2008 Pattern Recognition Letters  
In addition, the summarization capabilities were verified by comparison with several conventional spoken document summarization models.  ...  Various kinds of modeling structures and learning approaches were extensively investigated.  ...  information, (2) approaches based on statistical measures, and (3) approaches based on sentence classification.  ... 
doi:10.1016/j.patrec.2007.10.022 fatcat:dpyir3s2yncl5l7x25crgmcz2u

Spoken document summarization using relevant information

Yi-Ting Chen, Shih-Hsiang Lin, Hsin-Min Wang, Berlin Chen
2007 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)  
The results of experiments on Chinese broadcast news compiled in Taiwan show that the new methods outperform the previous HMM approach.  ...  framework for extractive spoken document summarization.  ...  3) approaches based on the generative probability.  ... 
doi:10.1109/asru.2007.4430107 dblp:conf/asru/ChenLWC07 fatcat:cxluno6c7nfnnbc3wgcd54u45e

A Text Summarization Method Based on Semantic Similarity Among Sentences

Yu-bing HOU
2020 DEStech Transactions on Social Science Education and Human Science  
In recent years, more and more attention has been paid to the graph-based text summarization.  ...  Through the evaluation on DUC datasets, performance of this approach shows greater improvement compared with a number of baseline systems.  ...  On account of using LDA topic model, we choose to use the document topic probability distribution to represent the topic, and in Section 3.1, we have obtained the sentence topic probability distribution  ... 
doi:10.12783/dtssehs/ecemi2020/34692 fatcat:ta3vgjs5dnb23mlnv2rqksgjhe

A Bayesian Method to Incorporate Background Knowledge during Automatic Text Summarization

Annie Louis
2014 Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
We develop systems for generic and update summarization based on this idea.  ...  We present a model based on Bayesian surprise which provides an intuitive way to identify surprising information from a summarization input with respect to a background corpus.  ...  Surprise is computed based on the changes in probabilities of all of these hypotheses upon seeing the summarization input.  ... 
doi:10.3115/v1/p14-2055 dblp:conf/acl/Louis14 fatcat:wkhqcslisbainhmgyb34dpicq4

ES-LDA: Entity Summarization using Knowledge-based Topic Modeling

Seyed Amin Pouriyeh, Mehdi Allahyari, Krys Kochut, Gong Cheng, Hamid R. Arabnia
2017 International Joint Conference on Natural Language Processing  
Entity summarization, which aims to create summaries for real world entities, has gained increasing attention in recent years.  ...  With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably.  ...  We rank the triples based on their probability distributions and choose the top-k triples that best describe the underlying entity as its summary.  ... 
dblp:conf/ijcnlp/PouriyehAKCA17 fatcat:4nybivqwyrd4dfzvyrhnc3uyje

A comparative study of probabilistic ranking models for spoken document summarization

Shih-Hsiang Lin, Yi-Ting Chen, Hsin-Min Wang, Berlin Chen
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
In the paper, we present a comparative study of various supervised and unsupervised probabilistic ranking models for spoken document summarization on the Chinese broadcast news.  ...  The purpose of extractive document summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio  ...  Extractive spoken document summarization may roughly fall into three main categories: 1) approaches based on the sentence structure or location information, 2) approaches based on statistical measures,  ... 
doi:10.1109/icassp.2008.4518787 dblp:conf/icassp/LinCWC08 fatcat:izqyktp5hze7xfgr4iqugt2com

Latent dirichlet allocation based multi-document summarization

Rachit Arora, Balaraman Ravindran
2008 Proceedings of the second workshop on Analytics for noisy unstructured text data - AND '08  
Extraction based Multi-Document Summarization Algorithms consist of choosing sentences from the documents using some weighting mechanism and combining them into a summary.  ...  Finally we present the evaluation of the algorithms on the DUC 2002 Corpus multi-document summarization tasks using the ROUGE evaluator to evaluate the summaries.  ...  In this we propose a novel approach of using LDA to capture the topics that the documents are based on.  ... 
doi:10.1145/1390749.1390764 dblp:conf/sigir/AroraR08 fatcat:7fjxnhytszevhlcxtmik4adlfq

Multi-Document Summarization from First Principles

William M. Darling
2010 Text Analysis Conference  
Our extractive summarization system is based on word frequency statistics similar to the SumBasic method.  ...  We present SumBasic+, a powerful multi-document summarization system built from first principles.  ...  ACKNOWLEDGMENT The author would like to thank his Ph.D. advisor Fei Song for his ideas and encouragement, and Lucy Vanderwende for taking the time to respond to several summarization related queries.  ... 
dblp:conf/tac/Darling10 fatcat:a5cat6ztyzgxpecxcp6fc6uhla

Multi-topic based Query-oriented Summarization [chapter]

Jie Tang, Limin Yao, Dewei Chen
2009 Proceedings of the 2009 SIAM International Conference on Data Mining  
Experimental results on two different genres of data show that our proposed approach can effectively extract a multi-topic summary from a document collection and the summarization performance is better  ...  Moreover, most of existing work assumes that documents related to the query only talks about one topic.  ...  The extraction-based document summarization method ranks sentences by their scores and selects ones with the highest scores as summaries.  ... 
doi:10.1137/1.9781611972795.98 dblp:conf/sdm/TangYC09 fatcat:5uhahbdm5bhilaeg5r3ikodyye

A New Text Summarization Approach based on Relative Entropy and Document Decomposition

Nawaf Alharbe, Mohamed Ali Rakrouki, Abeer Aljohani, Mashael Khayyat
2022 International Journal of Advanced Computer Science and Applications  
This automatic text summarization is document decomposition according to relative entropy analysis; which means measuring the difference of the probability distribution to measure the correlation between  ...  The performance demonstrated the efficiency of using the relative entropy of the topic probability distribution over sentences, which enriched the horizon of text processing and summarization research  ...  There are roughly three types: One is an approach based on expectation advancement, one is based on variational EM solving, and one is based on Gibbs sampling [24] .  ... 
doi:10.14569/ijacsa.2022.0130372 fatcat:grsgvweb4jf5xoaw52rszxijwi
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