Applying regression models to query-focused multi-document summarization

Ouyang, You; Li, Wenjie; Li, Sujian; Lu, Qin

doi:10.1016/j.ipm.2010.03.005

Cited by 146 publications

(81 citation statements)

References 10 publications

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“…You Ouyang et.al [16] used SVR (Support Vector Regression) to calculate the importance of the sentences in a given document. Another query focused summarization, multi document summarization done by Carbonell, J., & Goldstein in [17].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Yadav

Sharan

2015

International Journal of Information Retrieval Research

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Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score.

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Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Yadav

Sharan

2015

International Journal of Information Retrieval Research

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“…The sentence-ranking score obtained by this process pointed the query biased informativeness of the sentence and sentences with high ranks are selected to form the summary. In [12], regression models are employed to focus query in multi-document summarization.…”

Section: Related Workmentioning

confidence: 99%

A Topic-driven Summarization using K-mean Clustering and Tf-Isf Sentence Ranking

Wadhvani¹,

Pateriya²,

Roy³

2013

IJCA

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Enormous online information is available due to the World Wide Web. This needed efficient and accurate summarization systems to extract significant information. Text summarization system automatically generates a summary of a given document and helps people to make effective decisions in less time. In this paper two methods have been proposed for query-focused multi-document summarization that uses k-mean clustering, term-frequency and inversesentence-frequency method for sentence weighting to rank the sentences of the documents with respect to a given query. The proposed method finds the proximity of documents and query, and later uses this proximity to rank sentences of each document. It is assumed that the document which is nearer to a query might contain more meaning full sentences with respect to the information need expressed by user's query further if a sentence contains rare query term than it is more informative than the sentences that contains frequent query term. Both methods first gives weights to documents according to their proximity and use these document weights to rank each of their sentences with tf-idf ranking function. A relative study for proposed methods has been done and experimental results shows that both methods are comparable because of a slight difference in performance. DUC 2007 test dataset and ROUGH-1.5.5 summarization evaluation package is used for evaluation purpose.

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“…An ontology based approach to implement recommendation system that involves applying innovative web usage mining on log system to discover all possible imminent navigation patterns of current user and resolve any uncertainties in discovering the navigation pattern by applying ontological concept based similarity comparison and scoring algorithm (Mohanraj and Chandrasekaran, 2011). Document summarization techniques are one way of helping people to find information effectively and efficiently (Ouyang et al, 2011). Methods like Genetic Algorithm (GA), Mathematical Regression (MR), Feed Forward Neural Network (FFNN), Probabilistic Neural Network (PNN) and Gaussian Mixture Model (GMM) are used for text summarization task.…”

Section: Related Workmentioning

confidence: 99%

“…The regression models are implemented using Support Vector Regression (SVR). SVR is a regression type of Support Vector Machine (SVM) (Ouyang et al, 2011). Usually errors occur in query-based opinionated summary for blog entries.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Unified Semantic Blog Mining Framework and Summarized Blog Retrieval

Sathianesan¹,

Sankaranarayanan²

2013

Journal of Computer Science

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In today's scenario, publishing the personal content and technical content has become very easy for the publishers, because of the readily available prominent social media called blog. Content developers, teachers, researchers post various articles relevant to their research or newly emerging topics. Blog content in one blog source may resemble the semantics of blog from other source. Readers, who are fascinated in reading the blog content, anticipate retrieving the blogs from different sources for their query. A large number of posts are available in the web. Hence the blog reader's task becomes very complex to search the relevant content for their query. This study introduces a novel idea to collect the blog using unified Semantic Blog Mining Framework (SBMF). SBMF collects blogs from different blog sources using the ontology constructed for education domain. Blogs collected from different sources are collection which contains relevant or irrelevant blogs. The new blog summarizer summarizes the blog content and ranks the blogs according to the similarity of the blog with query given by the user. The proposed blog summarizer check the similarity of each sentence in a blog, sort the order of sentence based on the similarity of the text with query word and reduces the number of sentences. The experimental results shows that the proposed unified SBMF and blog summarizer produces better relevant and summarized number of blogs compared to various search engines. SBMF confirms the hundred percentage relevancy compared to other blog search engines. Blog summarizer yields accurate summarization for the collected blogs.

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Applying regression models to query-focused multi-document summarization

Cited by 146 publications

References 10 publications

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

A Topic-driven Summarization using K-mean Clustering and Tf-Isf Sentence Ranking

Unified Semantic Blog Mining Framework and Summarized Blog Retrieval

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