Citance-based retrieval and summarization using IR and machine learning
| Autor: | Avisha Das, Rakesh M. Verma, Luis F. T. Moraes, Samaneh Karimi, Azadeh Shakery |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Reference Document
Information retrieval Span (category theory) Series (mathematics) Computer science 05 social sciences General Social Sciences 02 engineering and technology Construct (python library) Library and Information Sciences Automatic summarization Computer Science Applications Task (project management) Set (abstract data type) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0509 other social sciences 050904 information & library sciences Textual entailment |
| Zdroj: | Scientometrics. 116:1331-1366 |
| ISSN: | 1588-2861 0138-9130 |
| DOI: | 10.1007/s11192-018-2785-8 |
| Popis: | 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 $$c \in C_D$$ , (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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink