Papers › Challenges in Data-to-Document Generation
Challenges in Data-to-Document Generation
Sam Wiseman, Stuart M. Shieber, Alexander M. Rush
Recent 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.
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Tasks
Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data-to-Text Generation | RotoWire | Encoder-decoder + conditional copy | BLEU | 14.19 | #6 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | RotoWire (Content Ordering) | Encoder-decoder + conditional copy | BLEU | 14.49 | #5 of 5 | Archive leaderboard | report |
| Data-to-Text Generation | RotoWire (Content Ordering) | Encoder-decoder + conditional copy | DLD | 8.68% | #5 of 5 | Archive leaderboard | report |
| Data-to-Text Generation | RotoWire (Relation Generation) | Encoder-decoder + conditional copy | Precision | 74.80% | #6 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | RotoWire (Relation Generation) | Encoder-decoder + conditional copy | count | 23.72 | #6 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | Rotowire (Content Selection) | Encoder-decoder + conditional copy | Precision | 29.49% | #5 of 5 | Archive leaderboard | report |
| Data-to-Text Generation | Rotowire (Content Selection) | Encoder-decoder + conditional copy | Recall | 36.18% | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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