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Neural Wikipedian: generating textual summaries from knowledge base triples

Neural Wikipedian: generating textual summaries from knowledge base triples
Neural Wikipedian: generating textual summaries from knowledge base triples
Most people need textual or visual interfaces in order to make sense of Semantic Web data. In this paper, we investigate the problem of generating natural language summaries for Semantic Web data using neural networks. Our end-to-end trainable architecture encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We explore a set of different approaches that enable our models to verbalise entities from the input set of triples in the generated text. Our systems are trained and evaluated on two corpora of loosely aligned Wikipedia snippets with triples from DBpedia and Wikidata, with promising results.
1570-8268
Vougiouklis, Pavlos
4cd0a8f1-c5e2-4ba2-8dcd-753db616b215
Elsahar, Hady
04528e31-9e9e-4de3-99ce-b6221889e912
Kaffee, Lucie-Aimée
8975c12f-9033-47ed-a2eb-b674b707c2ac
Gravier, Christophe
3d1a8495-afbd-4a61-b19b-a00036d4e74b
Laforest, Frederique
f61f682e-55a5-4626-a8d6-52aa2f3809d6
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Vougiouklis, Pavlos
4cd0a8f1-c5e2-4ba2-8dcd-753db616b215
Elsahar, Hady
04528e31-9e9e-4de3-99ce-b6221889e912
Kaffee, Lucie-Aimée
8975c12f-9033-47ed-a2eb-b674b707c2ac
Gravier, Christophe
3d1a8495-afbd-4a61-b19b-a00036d4e74b
Laforest, Frederique
f61f682e-55a5-4626-a8d6-52aa2f3809d6
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67

Vougiouklis, Pavlos, Elsahar, Hady, Kaffee, Lucie-Aimée, Gravier, Christophe, Laforest, Frederique, Hare, Jonathon and Simperl, Elena (2018) Neural Wikipedian: generating textual summaries from knowledge base triples. Web Semantics. (doi:10.1016/j.websem.2018.07.002).

Record type: Article

Abstract

Most people need textual or visual interfaces in order to make sense of Semantic Web data. In this paper, we investigate the problem of generating natural language summaries for Semantic Web data using neural networks. Our end-to-end trainable architecture encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We explore a set of different approaches that enable our models to verbalise entities from the input set of triples in the generated text. Our systems are trained and evaluated on two corpora of loosely aligned Wikipedia snippets with triples from DBpedia and Wikidata, with promising results.

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Submitted date: 29 November 2017
Accepted/In Press date: 27 July 2018
e-pub ahead of print date: 30 July 2018

Identifiers

Local EPrints ID: 422672
URI: https://eprints.soton.ac.uk/id/eprint/422672
ISSN: 1570-8268
PURE UUID: 51cc3f49-a4d6-41a7-b28b-6224bb04bfb1
ORCID for Lucie-Aimée Kaffee: ORCID iD orcid.org/0000-0002-1514-8505
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

Catalogue record

Date deposited: 30 Jul 2018 16:30
Last modified: 16 Oct 2018 00:33

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Contributors

Author: Hady Elsahar
Author: Lucie-Aimée Kaffee ORCID iD
Author: Christophe Gravier
Author: Frederique Laforest
Author: Jonathon Hare ORCID iD
Author: Elena Simperl ORCID iD

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