Citance-based retrieval and summarization using IR and machine learning

Samaneh Karimi (), Luis Moraes (), Avisha Das (), Azadeh Shakery () and Rakesh Verma ()
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Samaneh Karimi: University of Tehran
Luis Moraes: University of Houston
Avisha Das: University of Houston
Azadeh Shakery: University of Tehran
Rakesh Verma: University of Houston

Scientometrics, 2018, vol. 116, issue 2, No 34, 1366 pages

Abstract: Abstract We consider the three interesting problems posed by the CL-SciSumm series of shared tasks. Given a reference document D and a set $$C_D$$ C D of citances for D: (1) find the span of reference text that corresponds to each citance $$c \in C_D$$ c ∈ C D , (2) identify the facet corresponding to each span of reference text from a predefined list of five facets, and (3) construct a summary of at most 250 words for D based on the reference spans. The shared task provided annotated training and test sets for these problems. This paper describes our efforts and the results achieved for each problem, and also a discussion of some interesting parameters of the datasets, which may spur further improvements and innovations.

Keywords: Citance-based summarization; Structural correspondence learning; Positional language model; Textual entailment (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11192-018-2785-8

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