Neural Wikipedian: generating textual summaries from knowledge base triples

• DOI: 10.48550/arxiv.1711.00155
• Publication Date: 2018-01-01
• Authors: Vougiouklis, Pavlos; Elsahar, Hady; Kaffee, Lucie-Aimée; Gravier, Christophe; Laforest, Frederique; Hare, Jonathon; Simperl, Elena
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; 0202 electrical engineering, electronic engineering, information engineering; [INFO]Computer Science [cs]; 02 engineering and technology; Computation and Language (cs.CL); 01 natural sciences; 0105 earth and related environmental sciences

Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system 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 train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.