Summarize What You Are Interested In: An Optimization Framework for Interactive Personalized Summarization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most traditional summarization methods treat their outputs as static and plain texts, which fail to capture user interests during summarization because the generated summaries are the same for different users. However, users have individual preferences on a particular source document collection and obviously a universal summary for all users might not always be satisfactory. Hence we investigate an important and challenging problem in summary generation, i.e., Interactive Personalized Summarization (IPS), which generates summaries in an interactive and personalized manner. Given the source documents, IPS captures user interests by enabling interactive clicks and incorporates personalization by modeling captured reader preference. We develop experimental systems to compare 5 rival algorithms on 4 instinctively different datasets which amount to 5197 documents. Evaluation results in ROUGE metrics indicate the comparable performance between IPS and the best competing system but IPS produces summaries with much more user satisfaction according to evaluator ratings. Besides, low ROUGE consistency among these user preferred summaries indicates the existence of personalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it