Historical-Domain Pre-trained Language Model for Historical Extractive Text Summarization
Bibliographic record
Abstract
In recent years, pre-trained language models (PLMs) have shown remarkable advancements in the extractive summarization task across diverse domains.However, there remains a lack of research specifically in the historical domain.In this paper, we propose a novel method for extractive historical single-document summarization that leverages the potential of a domain-aware historical bidirectional language model, pre-trained on a large-scale historical corpus.Subsequently, we fine-tune the language model specifically for the task of extractive historical single-document summarization.One major challenge for this task is the lack of annotated datasets for historical summarization.To address this issue, we construct a dataset by collecting archived historical documents from the Centre Virtuel de la Connaissance sur l'Europe (CVCE) group at the University of Luxembourg.Furthermore, to better learn the structural features of the input documents, we use a sentence position embedding mechanism that enables the model to learn the position information of sentences.The overall experimental results on our historical dataset collected from the CVCE group show that our method outperforms recent state-of-the-art methods in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1 scores.To the best of our knowledge, this is the first work on extractive historical text summarization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".