Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering
Bibliographic record
Abstract
Page stream segmentation (PSS) is the task of retrieving the boundaries that separate source documents given a consecutive stream of documents (for example, sequentially scanned PDF files). The task has recently gained more interest as a result of the digitization efforts of various companies and organizations, as they move towards having all their documents available online for improved searchability and accessibility for users. The current state-of-the-art approach is neural start of document page classification on representations of the text and/or images of pages using models such as Visual Geometry Group-16 (VGG-16) and BERT to classify individual pages. We view the task of PSS as a clustering task instead, hypothesizing that pages from one document are similar to each other and different to pages in other documents, something that is difficult to incorporate in the current approaches. We compare the segmentation performance of an agglomerative clustering method with a binary classification model based on images on a new publicly available dataset and experiment with using either pretrained or finetuned image vectors as inputs to the model. To adapt the clustering method to PSS, we propose the switch method to alleviate the effects of pages of the same class having a high similarity, and report an improvement in the scores using this method. Unfortunately, neither clustering with pretrained embeddings nor clustering with finetuned embeddings outperformed start of document page classification for PSS. However, clustering with either pretrained or finetuned representations is substantially more effective than the baseline, with finetuned embeddings outperforming pretrained embeddings. Finally, having the number of documents K as part of the input, in our use case a realistic assumption, has a surprisingly significant positive effect. In contrast to earlier papers, we evaluate PSS with the overlap weighted partial match F1 score, developed as a Panoptic Quality in the computer vision domain, a metric that is particularly well-suited to PSS as it can be used to measure document segmentation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".