Identifying topic sentencehood

Philip M. Mccarthy, Adam M. Renner, Michael G. Duncan, Nicholas Duran, Erin J. Lightman, Danielle McNamara

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Four experiments were conducted to assess two models of topic sentencehood identification: the derived model and the free model. According to the derived model, topic sentences are identified in the context of the paragraph and in terms of how well each sentence in the paragraph captures the paragraph's theme. In contrast, according to the free model, topic sentences can be identified on the basis of sentential features without reference to other sentences in the paragraph (i.e., without context). The results of the experiments suggest that human raters can identify topic sentences both with and without the context of the other sentences in the paragraph. Another goal of this study was to develop computational measures that approximated each of these models. When computational versions were assessed, the results for the free model were promising; however, the derived model results were poor. These results collectively imply that humans' identification of topic sentences in context may rely more heavily on sentential features than on the relationships between sentences in a paragraph.

Original languageEnglish (US)
Pages (from-to)647-664
Number of pages18
JournalBehavior Research Methods
Volume40
Issue number3
DOIs
StatePublished - Aug 2008
Externally publishedYes

Fingerprint

Forensic Anthropology
Paragraph
Topic Sentences

ASJC Scopus subject areas

  • Psychology(all)
  • Psychology (miscellaneous)
  • Experimental and Cognitive Psychology

Cite this

Mccarthy, P. M., Renner, A. M., Duncan, M. G., Duran, N., Lightman, E. J., & McNamara, D. (2008). Identifying topic sentencehood. Behavior Research Methods, 40(3), 647-664. https://doi.org/10.3758/BRM.40.3.647

Identifying topic sentencehood. / Mccarthy, Philip M.; Renner, Adam M.; Duncan, Michael G.; Duran, Nicholas; Lightman, Erin J.; McNamara, Danielle.

In: Behavior Research Methods, Vol. 40, No. 3, 08.2008, p. 647-664.

Research output: Contribution to journalArticle

Mccarthy, PM, Renner, AM, Duncan, MG, Duran, N, Lightman, EJ & McNamara, D 2008, 'Identifying topic sentencehood', Behavior Research Methods, vol. 40, no. 3, pp. 647-664. https://doi.org/10.3758/BRM.40.3.647
Mccarthy PM, Renner AM, Duncan MG, Duran N, Lightman EJ, McNamara D. Identifying topic sentencehood. Behavior Research Methods. 2008 Aug;40(3):647-664. https://doi.org/10.3758/BRM.40.3.647
Mccarthy, Philip M. ; Renner, Adam M. ; Duncan, Michael G. ; Duran, Nicholas ; Lightman, Erin J. ; McNamara, Danielle. / Identifying topic sentencehood. In: Behavior Research Methods. 2008 ; Vol. 40, No. 3. pp. 647-664.
@article{0bab14ec633f4f33ad1025d18ce511f4,
title = "Identifying topic sentencehood",
abstract = "Four experiments were conducted to assess two models of topic sentencehood identification: the derived model and the free model. According to the derived model, topic sentences are identified in the context of the paragraph and in terms of how well each sentence in the paragraph captures the paragraph's theme. In contrast, according to the free model, topic sentences can be identified on the basis of sentential features without reference to other sentences in the paragraph (i.e., without context). The results of the experiments suggest that human raters can identify topic sentences both with and without the context of the other sentences in the paragraph. Another goal of this study was to develop computational measures that approximated each of these models. When computational versions were assessed, the results for the free model were promising; however, the derived model results were poor. These results collectively imply that humans' identification of topic sentences in context may rely more heavily on sentential features than on the relationships between sentences in a paragraph.",
author = "Mccarthy, {Philip M.} and Renner, {Adam M.} and Duncan, {Michael G.} and Nicholas Duran and Lightman, {Erin J.} and Danielle McNamara",
year = "2008",
month = "8",
doi = "10.3758/BRM.40.3.647",
language = "English (US)",
volume = "40",
pages = "647--664",
journal = "Behavior Research Methods, Instruments, and Computers",
issn = "1554-351X",
publisher = "Springer New York",
number = "3",

}

TY - JOUR

T1 - Identifying topic sentencehood

AU - Mccarthy, Philip M.

AU - Renner, Adam M.

AU - Duncan, Michael G.

AU - Duran, Nicholas

AU - Lightman, Erin J.

AU - McNamara, Danielle

PY - 2008/8

Y1 - 2008/8

N2 - Four experiments were conducted to assess two models of topic sentencehood identification: the derived model and the free model. According to the derived model, topic sentences are identified in the context of the paragraph and in terms of how well each sentence in the paragraph captures the paragraph's theme. In contrast, according to the free model, topic sentences can be identified on the basis of sentential features without reference to other sentences in the paragraph (i.e., without context). The results of the experiments suggest that human raters can identify topic sentences both with and without the context of the other sentences in the paragraph. Another goal of this study was to develop computational measures that approximated each of these models. When computational versions were assessed, the results for the free model were promising; however, the derived model results were poor. These results collectively imply that humans' identification of topic sentences in context may rely more heavily on sentential features than on the relationships between sentences in a paragraph.

AB - Four experiments were conducted to assess two models of topic sentencehood identification: the derived model and the free model. According to the derived model, topic sentences are identified in the context of the paragraph and in terms of how well each sentence in the paragraph captures the paragraph's theme. In contrast, according to the free model, topic sentences can be identified on the basis of sentential features without reference to other sentences in the paragraph (i.e., without context). The results of the experiments suggest that human raters can identify topic sentences both with and without the context of the other sentences in the paragraph. Another goal of this study was to develop computational measures that approximated each of these models. When computational versions were assessed, the results for the free model were promising; however, the derived model results were poor. These results collectively imply that humans' identification of topic sentences in context may rely more heavily on sentential features than on the relationships between sentences in a paragraph.

UR - http://www.scopus.com/inward/record.url?scp=50849111715&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50849111715&partnerID=8YFLogxK

U2 - 10.3758/BRM.40.3.647

DO - 10.3758/BRM.40.3.647

M3 - Article

C2 - 18697660

AN - SCOPUS:50849111715

VL - 40

SP - 647

EP - 664

JO - Behavior Research Methods, Instruments, and Computers

JF - Behavior Research Methods, Instruments, and Computers

SN - 1554-351X

IS - 3

ER -