Clustering top-ranking sentences for information access

A. Tombros, J. Jose, I. Ruthven, T. Koch (Editor), I.T. Solvberg (Editor)

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper we propose the clustering of top-ranking sentences (TRS) for effective information access. Top-ranking sentences are selected by a query-biased sentence extraction model. By clustering such sentences, we aim to generate and present to users a personalised information space. We outline our approach in detail and we describe how we plan to utilise user interaction with this space for effective information access. We present an initial evaluation of TRS clustering by comparing its effectiveness at providing access to useful information to that of document clustering.
LanguageEnglish
Title of host publicationResearch and AdvancedTechnology for Digital Libraries
Place of PublicationBerlin
Pages523-528
Number of pages5
DOIs
Publication statusPublished - 26 Jan 2004

Publication series

NameLecture Notes in Computer Science
PublisherLecture Notes in Computer Science, Springer

Keywords

  • top-ranking sentences
  • TRS
  • information access
  • extraction model

Cite this

Tombros, A., Jose, J., Ruthven, I., Koch, T. (Ed.), & Solvberg, I. T. (Ed.) (2004). Clustering top-ranking sentences for information access. In Research and AdvancedTechnology for Digital Libraries (pp. 523-528). (Lecture Notes in Computer Science). Berlin. https://doi.org/10.1007/b11967
Tombros, A. ; Jose, J. ; Ruthven, I. ; Koch, T. (Editor) ; Solvberg, I.T. (Editor). / Clustering top-ranking sentences for information access. Research and AdvancedTechnology for Digital Libraries. Berlin, 2004. pp. 523-528 (Lecture Notes in Computer Science).
@inbook{b327d26e09994435ab79715268a1fb27,
title = "Clustering top-ranking sentences for information access",
abstract = "In this paper we propose the clustering of top-ranking sentences (TRS) for effective information access. Top-ranking sentences are selected by a query-biased sentence extraction model. By clustering such sentences, we aim to generate and present to users a personalised information space. We outline our approach in detail and we describe how we plan to utilise user interaction with this space for effective information access. We present an initial evaluation of TRS clustering by comparing its effectiveness at providing access to useful information to that of document clustering.",
keywords = "top-ranking sentences, TRS, information access, extraction model",
author = "A. Tombros and J. Jose and I. Ruthven and T. Koch and I.T. Solvberg",
year = "2004",
month = "1",
day = "26",
doi = "10.1007/b11967",
language = "English",
isbn = "3-540-40726-3",
series = "Lecture Notes in Computer Science",
publisher = "Lecture Notes in Computer Science, Springer",
pages = "523--528",
booktitle = "Research and AdvancedTechnology for Digital Libraries",

}

Tombros, A, Jose, J, Ruthven, I, Koch, T (ed.) & Solvberg, IT (ed.) 2004, Clustering top-ranking sentences for information access. in Research and AdvancedTechnology for Digital Libraries. Lecture Notes in Computer Science, Berlin, pp. 523-528. https://doi.org/10.1007/b11967

Clustering top-ranking sentences for information access. / Tombros, A.; Jose, J.; Ruthven, I.; Koch, T. (Editor); Solvberg, I.T. (Editor).

Research and AdvancedTechnology for Digital Libraries. Berlin, 2004. p. 523-528 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Clustering top-ranking sentences for information access

AU - Tombros, A.

AU - Jose, J.

AU - Ruthven, I.

A2 - Koch, T.

A2 - Solvberg, I.T.

PY - 2004/1/26

Y1 - 2004/1/26

N2 - In this paper we propose the clustering of top-ranking sentences (TRS) for effective information access. Top-ranking sentences are selected by a query-biased sentence extraction model. By clustering such sentences, we aim to generate and present to users a personalised information space. We outline our approach in detail and we describe how we plan to utilise user interaction with this space for effective information access. We present an initial evaluation of TRS clustering by comparing its effectiveness at providing access to useful information to that of document clustering.

AB - In this paper we propose the clustering of top-ranking sentences (TRS) for effective information access. Top-ranking sentences are selected by a query-biased sentence extraction model. By clustering such sentences, we aim to generate and present to users a personalised information space. We outline our approach in detail and we describe how we plan to utilise user interaction with this space for effective information access. We present an initial evaluation of TRS clustering by comparing its effectiveness at providing access to useful information to that of document clustering.

KW - top-ranking sentences

KW - TRS

KW - information access

KW - extraction model

UR - http://www.springerlink.com/content/r5kv8rx43gcmml5q/

UR - http://dx.doi.org/10.1007/b11967

U2 - 10.1007/b11967

DO - 10.1007/b11967

M3 - Chapter

SN - 3-540-40726-3

T3 - Lecture Notes in Computer Science

SP - 523

EP - 528

BT - Research and AdvancedTechnology for Digital Libraries

CY - Berlin

ER -

Tombros A, Jose J, Ruthven I, Koch T, (ed.), Solvberg IT, (ed.). Clustering top-ranking sentences for information access. In Research and AdvancedTechnology for Digital Libraries. Berlin. 2004. p. 523-528. (Lecture Notes in Computer Science). https://doi.org/10.1007/b11967