Point at the Triple: Generation of Text Summaries from Knowledge Base Triples

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Pavlos Vougiouklis
Eddy Maddalena
Jonathon Hare
Elena Simperl


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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