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.
authors.affiliation: Institute for Intelligent Systems, University of Memphis, Memphis, Tennessee 38152, USA. pmccarthy@mail.psyc.memphis.edu
authors.forename: Philip M
authors.initials: PM
authors.lastname: McCarthy
authors.forename: Adam M
authors.initials: AM
authors.lastname: Renner
authors.forename: Michael G
authors.initials: MG
authors.lastname: Duncan
authors.forename: Nicholas D
authors.initials: ND
authors.lastname: Duran
authors.forename: Erin J
authors.initials: EJ
authors.lastname: Lightman
authors.forename: Danielle S
authors.initials: DS
authors.lastname: McNamara
mesh_terms: D003071:Cognition; D006801:Humans; D008037:Linguistics; D008568:Memory; D033262:Narration; D012660:Semantics