JRC's Participation at TAC 2011: Guided and Multilingual Summarization Tasks
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
The paper describes our participation in the Guided and Multilingual Summarization Tasks at the Text Analysis Conference 2011 (TAC’11). We participated in the Guided task with the system from the previous year which combines aspect identification by an event extraction system and automatically learned lexicons with LSA-based summarizer. This year we included temporal analysis to improve sentence ordering, detection of update information and dealing with the WHEN aspect. We made a first try to compress and paraphrase sentences with our second run. Multilingual summarization is our ultimate goal and thus all components of the system are either fully language independent or can be relatively easily adapted for other languages. The multilingual task provided a possibility to test the system on other languages then English. The sentence-extractive summarizer was ranked among the top systems in the case of readability and non-redundancy. Even if the content of its summaries was not ranked on the top for English in the main Guided task, it reached the top results in the Multilingual task. The generative run suffered from worse readability which affected also the content scores.
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 | 0.000 |
| Open science | 0.000 | 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