Bibliographic record
Abstract
This thesis presents a domain-driven approach to sports game summarization, a specific instance of large data-to-text generation (DTG). We first address the data fidelity issue in the Rotowire dataset by supplementing existing input records and demonstrating larger relative improvements compared to previously proposed purification schemes. As this method further increases the total number of input records, we alternatively formulate this problem as a multimodal problem (i.e. visual data-to-text), discussing potential advantages over purely textual approaches and studying its effectiveness for future expansion. We work exclusively with pre-trained end-to-end transformers throughout, allowing us to evaluate the efficacy of sparse attention and multimodal encoder-decoders in DTG and providing appropriate benchmarks for future work. \n \nTo automatically evaluate the statistical correctness of generated summaries, we also extend prior work on automatic relation extraction and build an updated pipeline that incorporates low amounts of human-annotated data which are quickly inflated via data augmentation. By formulating this in a ”text-to-text” fashion, we are able to take advantage of LLMs and achieve significantly higher precision and recall than previous methods while tracking three times the number of unique relations. Our updated models are more consistent and reliable by incorporating human-verified data partitions into the training and evaluation process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".