A novel unsupervised fine-tuning method for text summarization, and highlighting the limitations of ROUGE score
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 limited availability of datasets for text summarization tasks and their similar characteristics (e.g. news articles) make it crucial to focus on unsupervised learning techniques to enable summarization across different domains. Moreover, since summarization produces text output, effective methods developed for news articles can be applied to other domains lacking sufficient labelled data. This study introduces a novel target selection process to be used as an unsupervised learning method for fine-tuning text summarization models with unlabeled data. The process involves two-steps: first, generating an extractive summary (Ext-Reference) from the article, and second, using an abstractive model to create a pool of candidate summaries. The most suitable summary (to be used as the target) is then selected by calculating the cosine similarity between the Ext-Reference’s embedding and each candidate’s embedding. Furthermore, this project underscores the limitations of the ROUGE score, which assigns a relatively low score to this method. However, extended analysis with various metrics, including using GPT-4 as a judge, demonstrates the effectiveness of this technique for fine-tuning models without a specific target reference. It highlights the importance of using a combination of metrics, like those included in the SumEvaluator package released alongside this paper. SumEvaluator package on Github: https://github.com/AlaFalaki/SumEvaluator .
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 | 0.001 |
| Science and technology studies | 0.001 | 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