Has anyone else noticed that the Sudowrite 'Shorten' feature can be overly aggressive, to the point of damaging narrative or logical integrity? I'm approaching this from a backend perspective, but the core issue feels like a classic precision vs. recall problem in an ML model.
I was using it to condense a technical blog post draft—specifically, a section explaining a database connection pooling strategy. The feature consistently removed the *why* behind key configuration parameters (like `max_connections` and `pool_timeout`), leaving only the dry *what*. The resulting text was shorter, but the crucial argument for the chosen values was gone. It felt like pruning a query plan so severely that it changes the semantics of the operation.
This suggests the underlying model might be:
* Weighting sentence length and common words too heavily.
* Lacking context to identify 'crucial' vs. 'expendable' information within a specialized domain.
* Operating at too granular a level (sentence-by-sentence) rather than evaluating the paragraph's argumentative structure.
I'm curious about others' experiences, especially with non-fiction or technical writing. Are there reliable workarounds, or is it better to avoid 'Shorten' for anything with a necessary logical progression? Perhaps segmenting the text into smaller, more atomic units before applying the feature could help.
-- latency
sub-100ms or bust
Oh, absolutely. Your technical example is spot on - that's the exact frustration. I've seen this repeatedly while shortening marketing copy, particularly for complex product features. The "Shorten" function seems to target transitional phrases and explanatory clauses, which are often the connective tissue of an argument.
Just last week, I was condensing an explanation of an A/B test setup. The tool stripped out the hypothesis and the success metric rationale, leaving just the test variables and the call to view results. It made the text useless. Your point about it operating at a sentence level is key; it doesn't seem to evaluate the role a sentence plays in a larger logical chain.
I've had better luck with a two-step workaround: I use "Shorten" first, but then immediately use the "Expand" or "Rewrite" feature on the butchered sections to manually rebuild the missing logic. It's a bit of a dance. Have you tried iterating on the shortened output with other commands to guide it back?
Another trial, another spreadsheet
Your database connection pooling example is a perfect case study. It illustrates a fundamental evaluation gap in these "summarization as a service" features. They're often tuned on generic datasets (news articles, general web text) where the "why" is frequently redundant. In technical domains, the justification *is* the core content.
The sentence-level granularity hypothesis is likely correct. I'd add that it probably uses a form of salience scoring based on token probabilities, which demotes low-frequency, domain-specific terms - precisely the terms that carry the critical rationale. The model sees "pool_timeout" as salient, but "to prevent resource exhaustion during traffic spikes" gets a low score and is trimmed.
Have you tried benchmarking the feature's output against a known good extractive summarizer, like TextRank? It would isolate whether the issue is the compression method or the training objective.