TY - JOUR
T1 - Identifying topic sentencehood
AU - Mccarthy, Philip M.
AU - Renner, Adam M.
AU - Duncan, Michael G.
AU - Duran, Nicholas D.
AU - Lightman, Erin J.
AU - McNamara, Danielle S.
N1 - Funding Information:
This article is based on a presentation given at the 36th Annual Meeting of the Society for Computers in Psychology, Houston, TX, on November 16, 2006. This research was supported by the Institute for Education Sciences (IES R305G020018-02). Opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IES.
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.
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U2 - 10.3758/BRM.40.3.647
DO - 10.3758/BRM.40.3.647
M3 - Article
C2 - 18697660
AN - SCOPUS:50849111715
SN - 1554-351X
VL - 40
SP - 647
EP - 664
JO - Behavior Research Methods, Instruments, and Computers
JF - Behavior Research Methods, Instruments, and Computers
IS - 3
ER -