A reinforcement learning framework for answering complex questions
Bibliographic record
Abstract
Scoring sentences in documents given abstract summaries created by humans is important in extractive multi-document summarization. In this paper, we use extractive multi-document summarization techniques to perform complex question answering and formulate it as a reinforcement learning problem. We use a reward function that measures the relatedness of the candidate (machine generated) summary sentences with abstract summaries. In the training stage, the learner iteratively selects original document sentences to be included in the candidate summary, analyzes the reward function and updates the related feature weights accordingly. The final weights found in this phase are used to generate summaries as answers to complex questions given unseen test data. We use a modified linear, gradient-descent version of Watkins' Q(») algorithm with µ-greedy policy to determine the best possible action i.e. selecting the most important sentences. We compare the performance of this system with a Support Vector Machine (SVM) based system. Evaluation results show that the reinforcement method advances the SVM system improving the ROUGE scores by < 28%.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".