3 unstable releases

new 0.2.0	Feb 28, 2026
0.1.1	Feb 28, 2026
0.1.0	Feb 28, 2026

#1824 in Parser implementations

Used in trafilatura

BSD-2-Clause

1MB
1.5K SLoC

justext

Paragraph-level boilerplate removal for HTML.

A Rust port of JusText (v3.0.2) — classifies every paragraph in an HTML page as content or boilerplate using stopword density, link density, and text length, then refines classifications using neighbor context.

Usage

Add to your Cargo.toml:

[dependencies]
justext = "0.2"

use justext::{extract_text_lang, Config};

let html = r#"<html><body>
  <nav>Menu | About | Contact</nav>
  <article>
    <p>This is the main article body with enough text to be classified
    as content by the stopword density algorithm.</p>
  </article>
  <footer>Copyright 2024</footer>
</body></html>"#;

let text = extract_text_lang(html, "English", &Config::default()).unwrap();
println!("{text}");

For access to the full paragraph classification:

use justext::{justext_lang, Config};

let paragraphs = justext_lang(html, "English", &Config::default()).unwrap();

for p in &paragraphs {
    println!("{:?}  {}", p.class_type, p.text);
}

The _lang variants look up the stoplist internally. If you already have a stoplist, use justext() / extract_text() directly:

use justext::{extract_text, get_stoplist, Config};

let stoplist = get_stoplist("English").unwrap();
let text = extract_text(html, &stoplist, &Config::default());

How it works

Each paragraph goes through two stages:

Context-free classification — each paragraph is classified independently using link density, stopword density, and character length: Good, Bad, NearGood, or Short.
Context-sensitive revision — four passes use neighboring paragraph classes to promote or demote ambiguous paragraphs, resolving Short and NearGood into Good or Bad.

Paragraphs classified Good are content; everything else is boilerplate.

What the paragraph struct contains

Field	Description
`text`	Normalized text content
`class_type`	Final classification (`Good`, `Bad`, `NearGood`, `Short`)
`initial_class`	Context-free classification (before revision)
`dom_path`	Dot-separated DOM path, e.g. `"body.div.p"`
`xpath`	XPath with ordinals, e.g. `"/html[1]/body[1]/div[2]/p[1]"`
`words_count`	Whitespace-split word count
`chars_count_in_links`	Character count inside `<a>` tags
`tags_count`	Count of inline tags within the paragraph
`heading`	Whether the paragraph is a heading (`h0`–`h9` in dom_path)

Stoplists

100 languages are bundled and embedded at compile time. Retrieve one by name (case-insensitive):

let stoplist = get_stoplist("English").unwrap();   // HashSet<String>
let stoplist = get_stoplist("french").unwrap();    // case-insensitive

For language-independent extraction, pass an empty stoplist and zero thresholds:

use std::collections::HashSet;
use justext::{extract_text, Config};

let config = Config::default()
    .with_stopwords_low(0.0)
    .with_stopwords_high(0.0);

let text = extract_text(html, &HashSet::new(), &config);

To get the merged set of all stopwords across every language:

let all = get_all_stoplists(); // &'static HashSet<String>

Available languages:

Afrikaans, Albanian, Arabic, Aragonese, Armenian, Aromanian, Asturian, Azerbaijani, Basque, Belarusian, Belarusian_Taraskievica, Bengali, Bishnupriya_Manipuri, Bosnian, Breton, Bulgarian, Catalan, Cebuano, Chuvash, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian, Hebrew, Hindi, Hungarian, Icelandic, Ido, Igbo, Indonesian, Irish, Italian, Javanese, Kannada, Kazakh, Korean, Kurdish, Kyrgyz, Latin, Latvian, Lithuanian, Lombard, Low_Saxon, Luxembourgish, Macedonian, Malay, Malayalam, Maltese, Marathi, Neapolitan, Nepali, Newar, Norwegian_Bokmal, Norwegian_Nynorsk, Occitan, Persian, Piedmontese, Polish, Portuguese, Quechua, Romanian, Russian, Samogitian, Serbian, Serbo_Croatian, Sicilian, Simple_English, Slovak, Slovenian, Spanish, Sundanese, Swahili, Swedish, Tagalog, Tamil, Telugu, Turkish, Turkmen, Ukrainian, Urdu, Uzbek, Vietnamese, Volapuk, Walloon, Waray_Waray, Welsh, West_Frisian, Western_Panjabi, Yoruba

Configuration

All parameters default to the Python JusText 3.0.2 values.

let config = Config::default()
    .with_length_low(70)           // min chars for a non-short paragraph
    .with_length_high(200)         // min chars for stopword-dense paragraph to be Good
    .with_stopwords_low(0.30)      // min stopword density for NearGood
    .with_stopwords_high(0.32)     // min stopword density for Good/NearGood branch
    .with_max_link_density(0.2)    // max link-char ratio before Bad
    .with_max_heading_distance(200)// max chars to scan ahead when promoting headings
    .with_no_headings(false);      // set true to disable heading detection

Optional features

Feature	Description
`tracing`	Enable debug/trace logging (zero-cost when disabled)

justext = { version = "0.2", features = ["tracing"] }

Comparison to readability

	justext	libreadability
Unit of extraction	Paragraphs	DOM subtree
Output	`Vec<Paragraph>` (plain text)	Cleaned HTML
Approach	Stopword/link density heuristics	DOM scoring
Works best on	Pages without clear `<article>` structure	Standard news/blog articles

The two are complementary. trafilatura uses readability first and falls back to JusText — this crate enables the same pattern in Rust.

Benchmarks

Rust vs Python (jusText) — full pipeline (parse + classify + revise):

Input	Rust	Python	Speedup
small (2 paragraphs, 733 B)	21 µs	202 µs	10x
medium (20 paragraphs, 5 KB)	98 µs	1.4 ms	14x
large (100 paragraphs, 34 KB)	604 µs	11.1 ms	18x

Output comparison

On a 925-file dataset (the trafilatura comparison corpus), Rust and Python produce identical extracted text on 99.4% of files (919/925).

Measured on Apple M4 Max, Rust 1.93, macOS 15.7.

Reproduce:

cargo bench                                        # speed benchmarks
cargo run --bin compare -- <html-dir>              # Rust output (JSONL)
python3 scripts/compare_python.py --html-dir ...   # Python output (JSONL)

License

BSD-2-Clause

Dependencies

~2.2–4.5MB
~73K SLoC