Papers › Table-to-text Generation by Structure-aware Seq2seq Learning
Table-to-text Generation by Structure-aware Seq2seq Learning
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, Zhifang Sui
Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Table-to-Text Generation | WikiBio | Field-gating Seq2seq + dual attention | BLEU | 44.89 | #1 of 4 | Archive leaderboard | report |
| Table-to-Text Generation | WikiBio | Field-gating Seq2seq + dual attention | ROUGE | 41.21 | #1 of 4 | Archive leaderboard | report |
| Table-to-Text Generation | WikiBio | Field-gating Seq2seq + dual attention + beam search | BLEU | 44.71 | #2 of 4 | Archive leaderboard | report |
| Table-to-Text Generation | WikiBio | Field-gating Seq2seq + dual attention + beam search | ROUGE | 41.65 | #2 of 4 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections