Creating and evaluating multi-document sentence extract summaries

Goldstein, Jade; Mittal, Vibhu O.; Carbonell, Jaime G.; Callan, Jamie

doi:10.1145/354756.354815

Cited by 57 publications

(36 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We are using this model in our implementation to handle redundancy. Another model MMR [23] is popularly used (especially in with a given query) to reduce redundancy. The MMR (Maximal Marginal Relevance) criteria, "strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting appropriate passages for text summarization".…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…As such, clearly GistSumm embeds a quite rudimental multidocument AS procedure. It differs, and ignores, most of the more sophisticated multi-document AS proposals, such as [17].…”

Section: Query-based Extracts Of Multi-documentsmentioning

confidence: 99%

“…The following abilities, suggested in [17], amongst others, deserve attention: (a) clustering, to group together both similar documents and passages that help finding relevant information; (b) targeting coverage adequacy, to deal with the main issues across documents; (c) minimizing redundancy, to recognize singularities across documents and convey only the most relevant passages in the summary; (d) identifying source inconsistencies (e.g., typos or incorrect information), to prevent their inclusion in a summary and, thus, avoid the decrease of the IR efficacy. From these, the only one that GistSumm aims to tackle is (b), in that it correlates information of all the documents with the gist sentence aiming at covering the main issues of the involved documents.…”

Section: Query-based Extracts Of Multi-documentsmentioning

confidence: 99%

Extract-biased pseudo-revelance feedback

Batista¹,

Rino²

2007

Int. Artif.

View full text Add to dashboard Cite

Successfully retrieving a web document is a twofold problem: having an adequate query that can usefully and properly help filtering relevant documents from huge collections, and presenting the user those that may indeed fulfill his/her needs. In this paper, we focus on the first issue -the problem of having a misleading user query. The aim of the work is to refine a query by using extracts instead of full documents. Extracts, in our context, are actually summaries of documents of a hitlist produced by an extractive automatic summarizer. Automatic summarization of single and multi-documents is explored through GistSumm, our Gist Summarizer, which is based on the gist of a document, hence its name. Results on pseudo-relevance feedback for the Portuguese CHAVE collection show that gist-based extracts may improve information retrieval.

show abstract

“…They are combined linearly with a weighting function. In [9] MMR is refined and used to summarize multiple documents. Instead of full documents, different passages or sentences are assigned a score.…”

Section: Introductionmentioning

confidence: 99%

Distributed Ranking Methods for Geographic Information Retrieval

Kreveld

Reinbacher

Arampatzis

et al. 2005

Developments in Spatial Data Handling

View full text Add to dashboard Cite

Geographic Information Retrieval is concerned with retrieving documents in response to a spatially related query. This paper addresses the ranking of documents by both textual and spatial relevance. To this end, we introduce distributed ranking, where similar documents are ranked spread in the list instead of consecutively. The effect of this is that documents close together in the ranked list have less redundant information. We present various ranking methods, efficient algorithms to implement them, and experiments to show the outcome of the methods.

show abstract

Creating and evaluating multi-document sentence extract summaries

Cited by 57 publications

References 13 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

Extract-biased pseudo-revelance feedback

Distributed Ranking Methods for Geographic Information Retrieval

Contact Info

Product

Resources

About