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Point at the triple: generation of text summaries from knowledge base triples

Point at the triple: generation of text summaries from knowledge base triples
Point at the triple: generation of text summaries from knowledge base triples
We investigate the problem of generating natural language summaries from knowledge base triples. Our approach is based on a pointer-generator network, which, in addition to generating regular words from a fixed target vocabulary, is able to verbalise triples in several ways. We undertake an automatic and a human evaluation on single and open-domain summaries generation tasks. Both show that our approach significantly outperforms other data-driven baselines.
1076-9757
1-31
Vougiouklis, Pavlos
4cd0a8f1-c5e2-4ba2-8dcd-753db616b215
Maddalena, Eddy
397dbaba-4363-4c11-8e52-4a7ba4df4bae
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Vougiouklis, Pavlos
4cd0a8f1-c5e2-4ba2-8dcd-753db616b215
Maddalena, Eddy
397dbaba-4363-4c11-8e52-4a7ba4df4bae
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67

Vougiouklis, Pavlos, Maddalena, Eddy, Hare, Jonathon and Simperl, Elena (2020) Point at the triple: generation of text summaries from knowledge base triples. Journal of Artificial Intelligence Research, 69, 1-31. (doi:10.1613/jair.1.11694).

Record type: Article

Abstract

We investigate the problem of generating natural language summaries from knowledge base triples. Our approach is based on a pointer-generator network, which, in addition to generating regular words from a fixed target vocabulary, is able to verbalise triples in several ways. We undertake an automatic and a human evaluation on single and open-domain summaries generation tasks. Both show that our approach significantly outperforms other data-driven baselines.

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Accepted/In Press date: 23 June 2020
e-pub ahead of print date: 3 September 2020
Published date: September 2020
Learn more about Vision, Learning and Control research Learn more about School of Electronics and Computer Science research

Identifiers

Local EPrints ID: 443574
URI: http://eprints.soton.ac.uk/id/eprint/443574
DOI: doi:10.1613/jair.1.11694
ISSN: 1076-9757
PURE UUID: 8c6223f1-844e-49cd-8c64-bb9b87a20844
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

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Date deposited: 03 Sep 2020 01:46
Last modified: 17 Mar 2024 03:05

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Contributors

Author: Pavlos Vougiouklis
Author: Eddy Maddalena
Author: Jonathon Hare ORCID iD
Author: Elena Simperl ORCID iD

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