Challenges in Data-to-Document Generation
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CitationWiseman, Sam, Stuart M. Shieber and Alexander M. Rush. 2017. Challenges in Data-to-Document Generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP2017), Copenhagen, Denmark, September 7-11, 2017: 2243–2253.
AbstractRecent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task. In particular, we introduce a new, large-scale corpus of data records paired with descriptive documents, propose a series of extractive evaluation methods for analyzing performance, and obtain baseline results using current neural generation methods. Experiments show that these models produce fluent text, but fail to convincingly approximate human-generated documents. Moreover, even templated baselines exceed the performance of these neural models on some metrics, though copy- and reconstruction-based extensions lead to noticeable improvements.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:33927876
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