🧠 Topic modelling: what the literature said, and what this corpus said back

Status: discussion, not a specification. Written 2026-08-21. Updated 2026-08-23. It records where the topic stage's current shape came from: which published findings argued for it, which measurements on this project's own corpus confirmed or contradicted them, and which decisions are still open. docs/CONFIG.md is the reference for the settings themselves; TOPIC-DISCOVERY.md covers what happens after the model runs, meaning the topic graph and the discovery feature reading it. The discovery half borrows from a different set of sources than the modelling half below (OpenScholar's retrieval cascade, MiniRAG's heterogeneous graph, Reciprocal Rank Fusion, FlashRAG's extractive refiner, legacy AutoRAG's gold-set methodology). Those are quoted where each mechanism is described in TOPIC-DISCOVERY.md and itemised, borrowings and refusals both, in INSPIRATION.md. This document's own reference list therefore stays what it was: the evidence behind the model.

On the references below. The four sources here are not in content/ledger.sqlite and therefore have no citekeys, so they are cited in full at the end and by author-year in the text. That is deliberate, and it is the rule from AGENTS.md: a citekey exists only when the human's own .bib export and a real parse of a real PDF put it in the ledger. Inventing one here, in a document that ships, for sources that are genuinely real, is exactly the habit this project exists to make impossible. If these papers are later added to a library and synced, this document should be updated to cite them properly instead of being left as prose.

❗ The problem, in one sentence

content/topics.json existed for several releases with no consumer, and the topic model had no way to know that the corpus's owner had already grouped the same papers by hand in Zotero.

📚 What the sources argued, and what happened when it was tried

🌱 1. Seed the model, but do not let seeding replace discovery

Asta's Topic Extraction and Document-Topic Linking in Scientific and Domain-Specific Corpora is unambiguous that a narrow corpus wants guidance: fully unsupervised models "often produce overlapping or vague topics unless they are anchored by concepts, seed words, or expert knowledge", and seed-guided models are "useful when you have partial prior knowledge and still want the model to discover unknown topics rather than only match a fixed taxonomy". It notes in passing that keyATM "allows each document to belong to multiple keyword topics".

This stage now has that design, but the first implementation got it wrong in a way the survey's phrasing predicts. Routing seed phrases through BERTopic's zeroshot_topic_list makes seeding and discovery compete for the same documents: measured on this corpus, nine seed phrases took the emergent topic count from 81 to 53, roughly three discovered topics traded away per named one. Pushed further it stops trading and breaks: enough zero-shot assignment leaves HDBSCAN fewer points than its own min_samples needs and the fit dies inside sklearn.

The resolution is that seeds never touch the clustering. They are matched against the same document vectors afterwards. A seed list is therefore unlimited and costs nothing, which is the property the survey asks for and the zero-shot path could not give.

🧹 2. Preprocessing is a modelling step, not a cleanup step

Asta's first best practice for narrow scientific domains, and its warning is specific: "generic preprocessing is often too destructive", because in-domain corpora carry "abbreviations, identifiers, formulas, and rare phrases" that hold most of the meaning. It cites industrial work (Nikulski et al., 2025) reporting a 22% gain from domain preprocessing plus a domain-adapted encoder, and stresses that preprocessing is "a complex, high-effort part of the system, not a trivial step".

doc_vectors.content_text() follows that literally. It removes the reference list, bare emails, URLs, DOIs, copyright lines and page numbers, and nothing else. It does no stop-word removal, lowercasing or low-frequency filtering, each of which would destroy exactly the multiword domain terms this corpus is discriminated by. A test asserts IEC 62304, MQTT v5 and DTaaS survive untouched.

What it fixed, measured: a References heading is detectable in 451 of 497 documents (91%); the text after it is a median 15% of the document. Two artefact clusters left the top twenty: www nature, program officer, national academies and a116, a115, a114.

What it did not fix. The author cluster survived. werner kritzinger, fraunhofer austria was still a top-three topic afterwards, because the name is in the body, not the bibliography: kritzinger appears in 101 documents and 55 still contain it after the reference list is removed. Those papers genuinely are a topic (they survey the digital-twin/shadow/model taxonomy), so the clustering is right and only the label is wrong. No amount of further preprocessing fixes that without deleting content that belongs to the topic. It is a labelling problem, which §3 answers.

🏷 3. Label topics from recognised domain terms

Asta's third best practice: "use domain term recognition as a backbone for both topics and labels", citing TLATR (Truica et al., 2021), which extracts domain-specific terms first and then chooses each topic's label from the recognised terms by C-Value scoring, and Silvello et al. (2016), which ranks candidates by both corpus-level and document-level TF-IDF.

This is built, as chitragupta/enrich/topic_labels.py. It fixed two things at once: the author-name labels above, and BERTopic's own topic names, which on this corpus were stopwords (0_the_and_of_to) because no CountVectorizer(stop_words=...) was configured. A person's name is not a domain term and is excluded by construction: every surname in the corpus's own bibliography (1,277 of them) is dropped from the label vocabulary, behind [enrich].topic_exclude_author_names for a corpus where that trade is not worth it.

📄 4. A prefix is the wrong part of a long document

Koh et al. (2022) measure the layout bias that makes truncation tolerable for short documents (roughly 60% of salient sentences in the first 30%) and find it absent in long ones, where salient content is scattered: uniformity 0.89-0.93 for long-document benchmarks against 0.78-0.86 for short. Models that truncate "suffer significant performance degradation" there.

This corpus is squarely long-document. Measured, two representative papers run to 22,048 and 24,132 tokens against a 512 word-piece model limit, so model.encode(text) was embedding about 2% of each paper: its chapter heading, author list and abstract opening. Documents are now chunked with embed_index.chunk_text() and the chunk vectors mean-pooled.

The measured effect, at identical clustering settings, is 75 topics with median size 5 (prefix) against 81 with median 4 (pooled), and labels that stop being front matter, with californium, coap, mqtt v5, sparkplug separating from mbdo, twinops, moddevops, aadl from gitops, dataops, iec 62304. The cost is a higher outlier share (10% to 17%), which is expected: averaging 82 chunks pulls every vector toward the corpus centroid and reduces separation.

🔭 5. Multi-document summarisation is the shape of a topic

Ma et al. (2022) define multi-document summarisation as generating a summary "from a cluster of topic-related documents", and name cross-document redundancy as its defining problem. A topic here has exactly that shape, which makes per-topic summarisation the natural consumer for this artefact, and chitragupta/overlap_index.py already computes shared n-grams corpus-wide, so the redundancy detector exists.

One constraint from Koh et al. bears directly on it, and it is a SOUL.md argument, not a quality one: abstractive models carry factual inconsistencies in up to 30% of outputs, while extractive summarisation "will faithfully preserve the original content". A topic summary asserting a claim no paper made is the same failure class as a fabricated citekey, one level up. Extractive first; abstractive only behind a human accept-gate, if at all.

🏗 6. Structural labels would let content be selected, if they existed

DocBank (Li et al., 2020) annotates 500,000 document pages at token level with twelve semantic labels (Abstract, Author, Caption, Date, Equation, Figure, Footer, List, Paragraph, Reference, Section, Table), built by weak supervision over arXiv LaTeX, with a LayoutLM baseline at 0.935 macro-average, under Apache-2.0.

That taxonomy is the vocabulary §2 needed and could not express, because Docling emits only three labels on this corpus: text (77% of characters), list_item (21%) and section_header (2%). References, captions, equations and author blocks are one undifferentiated category, which is why content_text() has to find the bibliography by heading regex and not by label. Adopting DocBank-grade structure means a LayoutLM-class model and Detectron2: a real dependency and its own piece of work, not a configuration change.

This was filed as an issue of its own and is now closed as not planned, because §3 removed the evidence for it. The artefact clusters that motivated it (an author block, a publisher's front matter, a run of clause numbers) all left the top twenty once topic labels stopped being drawn from author names and citation scaffolding, and that cost no new dependency at all. What survives is a DOI fragment and a broken fi ligature, and the ligature is a text-extraction defect, not a layout one: it corrupts identification and classification into identi fi cation, which damages the clustering input and not merely the display. DocBank would not touch it.

If structural extraction is wanted later, the better starting point is GROBID-CITATION-GRAPH.md, not that issue. GROBID is purpose-built for the two spans content_text() finds by heading regex (the author/affiliation block and the reference list). It returns them as structured records at roughly 0.87-0.90 F1 instead of as a class label per token, and does sequence labelling instead of layout inference, so it costs a fraction of a Docling parse per document. That proposal is itself unbuilt and argues a case for a citation graph, with structural extraction a by-product; it is the document to start from, and is not a queued piece of work.

✅ 7. Validate stability, not just fit

Asta again: hyperparameter choice in this family is usually driven by coherence alone, but "a model that looks good once can still be unstable across runs or topic counts", so good evaluation combines coherence with topic diversity, stability, and whether experts can name the topics. It cites work using repeated resampling and model selection (Yengejeh et al., 2026, which lands on 83-88 topics for 3,689 forensic-science abstracts).

This is built: bench/bench_topic_depth.py --repeats refits each setting on 90% bootstrap resamples and scores agreement with the full fit by adjusted Rand index. The old hardcoded defaults (n_neighbors=15, min_cluster_size=10) scored 0.14, barely more stable than chance; the shipped defaults (5/3/2) score 0.80.

Both of those figures hold UMAP fixed, which the shipped stage does not (a recorded issue). They refit HDBSCAN on each resample over a reduction fitted once, so they measure the stability of the clustering given a reduction, and not of the whole pipeline. bench_topic_depth.py now reports both arms as separate columns, and the gap is large: measured 2026-09-07 over the full grid, the UMAP-fixed arm spans 0.56--0.82 and the full-refit arm spans 0.35--0.51. Roughly half the apparent stability belongs to the dimensionality reduction.

Read the 0.14 -> 0.80 improvement with that in mind. On the full-refit arm the old settings score 0.35 and the shipped defaults 0.37, which with ten resamples and no error bars is no measured difference. What the parameter change demonstrably bought was granularity (6 topics against 79) and a lower outlier share, not stability. The 0.14 itself is also not comparable to anything measured today: the corpus has changed since, and the same settings now score 0.68 on the UMAP-fixed arm. bench/RESULTS.md's 2026-09-07 (B4b) entry has the grid. chitragupta/enrich/topic_model.py's own docstring still admits topic ids are not stable between runs (membership has to be read by label or citekey, not id), but which settings reproduce is now measured instead of assumed.

📊 Where the numbers come from

• bench/bench_topic_depth.py: the granularity sweep behind topic_min_cluster_size and its siblings.
• bench/bench_topic_membership.py: the five-mechanism comparison behind HDBSCAN soft clustering.

A correction worth keeping, because it bit twice. An early version of this comparison scored HDBSCAN's own soft clustering at 1% agreement, which is nonsense: it indexed HDBSCAN's cluster ids with BERTopic's topic ids, and BERTopic renumbers topics by size (cluster 0 was topic 6 on the run this was written against). Separately, an earlier ad-hoc measurement reported centroid rules agreeing only 30-45% of the time. That is true at 7 topics on a seeded run, and not true at 76, where every non-degenerate mechanism agrees 100%. Agreement discriminates in the first regime and not the second; concentration does both. Quote the committed script's numbers, not either of those.

Both are recorded in bench/RESULTS.md. Every figure quoted above is from this project's own 497-document corpus on one host, the same limitation every section of bench/RESULTS.md carries.

📖 References

These are cited in full because none is in the ledger; see the note at the top.

• Koh, H. Y., Ju, J., Liu, M., and Pan, S. (2022). An Empirical Survey on Long Document Summarization: Datasets, Models, and Metrics. ACM Computing Surveys 55(8), Article 154. https://doi.org/10.1145/3545176
• Ma, C., Zhang, W. E., Guo, M., Wang, H., and Sheng, Q. Z. (2022). Multi-document Summarization via Deep Learning Techniques: A Survey. ACM Computing Surveys 55(5), Article 102. https://doi.org/10.1145/3529754
• Asta (Allen Institute for AI). Topic Extraction and Document-Topic Linking in Scientific and Domain-Specific Corpora. Generated research synthesis, supplied 2026-08-21. Not a peer-reviewed publication; the primary sources it cites and which are named above (Nikulski et al. (2025), Truica et al. (2021), Silvello et al. (2016), Yengejeh et al. (2026)) are reported at second hand and have not been read directly.
• DocBank. Li, M., Xu, Y., Cui, L., Huang, S., Wei, F., Li, Z., and Zhou, M. (2020). DocBank: A Benchmark Dataset for Document Layout Analysis. https://github.com/doc-analysis/DocBank, Apache-2.0.