2009Unpublished venueRequires access

Generating synopses for document-element search

Sumit Bhatia, Shibamouli Lahiri, Prasenjit Mitra

Open publisher page 13 citations

Abstract

Scientists often search for document-elements like tables, figures, or algorithm pseudo-codes. Domain scientists and researchers report important data, results and algorithms using these document-elements; readers want to compare the reported results with their findings. Some document-element search engines have been proposed (especially to search for tables and figures) to make this task easier. While searching for document-elements today, the end-user is presented with the caption of the document-element and a sentence in the document text that refers to the document-element. Oftentimes, the caption and the reference text do not contain enough information to interpret the document-element. In this paper, we present the first set of methods to extract this useful information (synopsis) related to document-elements automatically. We also investigate the problem of choosing the optimum synopsis-size that strikes a balance between information content and size of the generated synopses.

About this research paper

What this paper is about

Scientists often search for document-elements like tables, figures, or algorithm pseudo-codes. Domain scientists and researchers report important data, results and algorithms using these document-elements; readers want to compare the reported results with their findings. Some document-element search engines have been proposed (especially to search for tables and figures) to make this task easier. While searching for document-elements today, the end-user is presented with the caption of the document-element and a sentence in the document text that refers to the document-element. Oftentimes, the caption and the reference text do not contain enough information to interpret the document-element. In this paper, we present the first set of methods to extract this useful information (synopsis) related to document-elements automatically. We also investigate the problem of choosing the optimum synopsis-size that strikes a balance between information content and size of the generated synopses.

Why it matters

OpenAlex reports 13 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Scientists often search for document-elements like tables, figures, or algorithm pseudo-codes. Domain scientists and researchers report important data, results and algorithms using these document-elements; readers want to compare the reported results with their findings. Some document-element search engines have been proposed (especially to search for tables and figures) to make this task easier. While searching for document-elements today, the end-user is presented with the caption of the document-element and a sentence in the document text that refers to the document-element. Oftentimes, the caption and the reference text do not contain enough information to interpret the document-element. In this paper, we present the first set of methods to extract this useful information (synopsis) related to document-elements automatically. We also investigate the problem of choosing the optimum synopsis-size that strikes a balance between information content and size of the generated synopses.

Key concepts: Computer science, Information retrieval, Element (criminal law), Set (abstract data type), Task (project management), Sentence, Document retrieval, Source document

Related papers

Back to paper searchBrowse research topicsOriginal source
Generating synopses for document-element search — Research Paper | ScholarLens