Bibliographic record
Abstract
Tables, ubiquitous in data-oriented documents like scientific papers and financial statements, organize and convey relational information. Automatic table recognition from document images, which involves detection within the page, structural segmentation into rows, columns, and cells, and information extraction from cells, has been a popular research topic in document image analysis (DIA). With recent advances in natural language generation (NLG) based on deep neural networks, data-to-text generation, in particular for table summarization, offers interesting solutions to time-intensive data analysis. In this paper, we aim to bridge the gap between efforts in DIA and NLG regarding tabular data: we propose WEATHERGOV+, a dataset building upon the WEATHERGOV dataset, the standard for tabular data summarization techniques, that allows for the training and testing of end-to-end methods working from input document images to generate text summaries as output. WEATHERGOV+ contains images of tables created from the tabular data of WEATHERGOV using visual variations that cover various levels of difficulty, along with the corresponding human-generated table summaries of WEATHERGOV. We also propose an end-to-end pipeline that compares state-of-the-art table recognition methods for summarization purposes. We analyse the results of the proposed pipeline by evaluating WEATHERGOV+ at each stage of the pipeline to identify the effects of error propagation and the weaknesses of the current methods, such as OCR errors. With this research (dataset and code available here1), we hope to encourage new research for the processing and management of inter- and intra-document collections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.145 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".