A French Human Reference Corpus for Multi-Document Summarization and Sentence Compression
Bibliographic record
Abstract
This paper presents two corpora produced within the RPM2 project: a multi-document summarization corpus and a sentence compression corpus.Both corpora are in French.The first one is the only one we know in this language.It contains 20 topics with 20 documents each.A first set of 10 documents per topic is summarized and then the second set is used to produce an update summarization (new information).4 annotators were involved and produced a total of 160 abstracts.The second corpus contains all the sentences of the first one.4 annotators were asked to compress the 8432 sentences.This is the biggest corpus of compressed sentences we know, whatever the language.The paper provides some figures in order to compare the different annotators: compression rates, number of tokens per sentence, percentage of tokens kept according to their POS, position of dropped tokens in the sentence compression phase, etc.These figures show important differences from an annotator to the other.Another point is the different strategies of compression used according to the length of the sentence.
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.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".