Citance-based retrieval and summarization using IR and machine learning

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 of citances for D : (1) find the span of reference text that corresponds to each citance c ∈ C D , (2) identify the facet corresponding to each span of reference text...

Full description

Saved in:
Bibliographic Details
Published in:Scientometrics 2018-08, Vol.116 (2), p.1331-1366
Main Authors: Karimi, Samaneh, Moraes, Luis, Das, Avisha, Shakery, Azadeh, Verma, Rakesh
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer Science
Information retrieval
Information Storage and Retrieval
Innovations
Learning algorithms
Library Science
Machine learning
Test sets
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 of citances for D : (1) find the span of reference text that corresponds to each citance 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.
ISSN:0138-9130
1588-2861
DOI:10.1007/s11192-018-2785-8