Extract duplicated document-aggregation and topic-naming patterns into shared helpers

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### Feature request

`_bertopic.py` contains several near-identical copy-paste patterns that have diverged slightly over time. Extracting them into shared helpers would reduce duplication and prevent future divergence.

Three patterns are affected:

1. **Document aggregation** — `documents.groupby(["Topic"], as_index=False).agg({"Document": " ".join})` appears in 4 methods: `_extract_topics`, `hierarchical_topics`, `update_topics`, and `partial_fit`. When a new column needs aggregating (e.g., images), every call site must be updated independently.

2. **Feature names extraction** — a 5-line sklearn version check (`get_feature_names` vs `get_feature_names_out`) is duplicated in `hierarchical_topics` and `_c_tf_idf`.

3. **Topic name from words** — `"_".join([x[0] for x in words][:N])` appears in 4 places across 2 methods (`topic_labels_` with `[:4]`, `hierarchical_topics` with `[:5]` in 3 places), with inconsistent slicing.

### Motivation

Reduce maintenance burden. Any change to the aggregation pattern (e.g., adding image support) currently requires updating 4 call sites in lock-step. The duplicated sklearn version check is a maintenance liability — when the old API is eventually dropped, two separate locations need updating. Inconsistent topic name slicing could produce subtly different results depending on the code path.

### Your contribution

I can submit a PR that extracts three private helpers:
- `_aggregate_documents(documents, columns=None)` — consolidates the groupby pattern
- `_get_feature_names(vectorizer)` — wraps the sklearn version check
- `_topic_name_from_words(words, n=5)` — consistent slicing

**Zero behavior change.** Each helper is a direct extraction of existing code. All existing tests pass without modification.

I've already been prototyping this in my fork, so I can turn it into a PR quickly if the direction looks good to you.

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Start in _bertopic.py with _extract_topics, hierarchical_topics, update_topics, partial_fit, and _c_tf_idf, comparing the duplicated aggregation, feature-name, and topic-name patterns. Extract the three proposed private helpers without behavior changes, then run the existing tests and confirm they still pass.

Written by the indexing model from the issue text.