Overview of the CLEF 2024 SimpleText Task 3: Simplify Scientific Text
Bibliographic record
Abstract
This article provides a comprehensive summary of the CLEF 2024 SimpleText Task 3, which focuses on simplifying scientific text based on specific queries. We discuss in detail the motivation for lay access to scholarly literature, and provide an overview of the setup of the scientific text simplification task. One of the main innovations of the CLEF 2024 SimpleText Task 3 is to complement sentence-level text simplification with a document-level text simplification task. We describe the resulting sentence-level and document-level text simplification test collection in detail, which consists of a corpus of over 1,500 paired source and reference sentences, and a corpus of over 250 paired source and reference abstracts, both containing the source text from scientific abstracts with direct reference simplifications produced by human annotators. We present the results of the participants submission, with 15 teams submitting 52 sentence-level text simplification runs and 9 teams submitting 31 sentence-level text simplification runs. The article concludes with an in-depth analysis, including information distortion and potential LLM “hallucinations” of the simplified sentences submitted by participants.
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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.025 |
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".