Repository navigation
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
On a saved HTML snapshot of the OAIC Privacy Act overview,
Document.summary()selects the feedback form'sfeedback__section-2-contentcontainer. The result contains prompts such as “Rate your experience” and omits the introduction to the Act and the overview of the 13 Australian Privacy Principles.To reproduce on
3e7a832, save the page's original HTML asoaic.htmland run:Article assembly moves the selected node into the output without its enclosing form, so removing
<form>elements during cleanup misses the feedback text. This change keeps the existing<article>and<main>recovery logic. If the resulting candidate is a form or is inside a form, and scored candidates exist outside forms, it reruns selection using those candidates.Validation
make pre-releasepassed on Python 3.13 with 63 passing tests. The 166-page benchmark scored F1 0.973253 before and after, with identical matched, extracted, and reference token counts on every page.