Add TrafilaturaExtractor class

docs/user-guide/api/download.rst

@@ -55,6 +55,9 @@ Common Crawl
 .. autoclass:: nemo_curator.download.ResiliparseExtractor
     :members:
 
+.. autoclass:: nemo_curator.download.TrafilaturaExtractor
+    :members:
+
 ------------------------------
 Wikipedia
 ------------------------------

docs/user-guide/download.rst

@@ -18,7 +18,7 @@ the extraction step to limit the amount of documents that undergo this heavy com
 NeMo Curator provides example utilities for downloading and extracting Common Crawl, ArXiv, and Wikipedia data.
 In addition, it provides a flexible interface to extend the utility to other datasets.
 Our Common Crawl example demonstrates how to process a crawl by downloading the data from S3, doing preliminary language filtering with pyCLD2,
-and extracting the relevant text with jusText or Resiliparse to output :code:`.jsonl` files.
+and extracting the relevant text with jusText, Resiliparse, or Trafilatura to output :code:`.jsonl` files.
 
 NeMo Curator currently does not provide out-of-the-box support for web-crawling or web-scraping.
 It provides utilities for downloading and extracting data from the preexisting online sources given above.
@@ -88,6 +88,7 @@ You can choose to modify the HTML text extraction algorithm used in ``download_c
   from nemo_curator import get_client
   from nemo_curator.download import (
       ResiliparseExtractor,
+      TrafilaturaExtractor,
       download_common_crawl,
   )
   from nemo_curator.datasets import DocumentDataset
@@ -106,8 +107,10 @@ You can choose to modify the HTML text extraction algorithm used in ``download_c
       output_type = "jsonl"
       os.makedirs(output_folder, exist_ok=True)
 
-      # Change the extraction algorithm to use ResiliparseExtractor
+      # Change the extraction algorithm to Resiliparse
       extraction_algorithm = ResiliparseExtractor()
+      # Alternatively, change the extraction algorithm to Trafilatura
+      # extraction_algorithm = TrafilaturaExtractor()
 
       # Download and extract the Common Crawl data using the Resiliparse extraction algorithm.
       # The function returns a DocumentDataset that contains the extracted documents.
@@ -128,15 +131,15 @@ You can choose to modify the HTML text extraction algorithm used in ``download_c
   if __name__ == "__main__":
       main()
 
-Above, we changed the extraction algorithm from the default ``JusTextExtractor``.
+Above, we changed the extraction algorithm from the default ``JusTextExtractor``. **Note:** Please see the Trafilatura documentation `here <https://trafilatura.readthedocs.io/en/latest/settings.html>`_
 
 The return value ``common_crawl`` will be in NeMo Curator's standard ``DocumentDataset`` format. Check out the function's docstring for more parameters you can use.
 
 NeMo Curator's Common Crawl extraction process looks like this under the hood:
 
  1. Decode the HTML within the record from binary to text.
  2. If the HTML can be properly decoded, then with `pyCLD2 <https://github.com/aboSamoor/pycld2>`_, perform language detection on the input HTML.
- 3. Finally, the extract the relevant text with `jusText <https://github.com/miso-belica/jusText>`_ or `Resiliparse <https://github.com/chatnoir-eu/chatnoir-resiliparse>`_ from the HTML and write it out as a single string within the 'text' field of a json entry within a `.jsonl` file.
+ 3. Finally, the extract the relevant text with `jusText <https://github.com/miso-belica/jusText>`_, `Resiliparse <https://github.com/chatnoir-eu/chatnoir-resiliparse>`_, or `Trafilatura <https://trafilatura.readthedocs.io/en/latest/>`_ from the HTML and write it out as a single string within the 'text' field of a json entry within a `.jsonl` file.
 * ``download_wikipedia`` will download and extract the latest wikipedia dump. Files are downloaded using ``wget``. Wikipedia might download slower than the other datasets. This is because they limit the number of downloads that can occur per-ip address.
 
   .. code-block:: python

nemo_curator/download/__init__.py

@@ -20,6 +20,7 @@
     CommonCrawlWARCIterator,
     JusTextExtractor,
     ResiliparseExtractor,
+    TrafilaturaExtractor,
     download_common_crawl,
 )
 from .doc_builder import (
@@ -54,6 +55,7 @@
     "CommonCrawlWARCDownloaderExtractOnly",
     "JusTextExtractor",
     "ResiliparseExtractor",
+    "TrafilaturaExtractor",
     "download_wikipedia",
     "WikipediaDownloader",
     "WikipediaIterator",

nemo_curator/download/commoncrawl.py

@@ -17,6 +17,7 @@
 import subprocess
 import unicodedata
 from abc import ABC, abstractmethod
+from copy import deepcopy
 from typing import Literal, Optional
 from urllib.parse import urlparse
 
@@ -25,6 +26,8 @@
 import pycld2 as cld2
 from charset_normalizer import detect
 from resiliparse.extract.html2text import extract_plain_text
+from trafilatura import extract as extract_with_trafilatura
+from trafilatura.settings import DEFAULT_CONFIG as TRAFILATURA_DEFAULT_CONFIG
 from warcio.archiveiterator import ArchiveIterator
 
 from nemo_curator.datasets import DocumentDataset
@@ -200,6 +203,98 @@ def extract_text(self, html, stop_words):
         return result
 
 
+class TrafilaturaExtractor(HTMLExtractorAlgorithm):
+    def __init__(
+        self,
+        required_stopword_density=0.32,
+        min_extracted_size=250,
+        min_extracted_comm_size=1,
+        min_output_size=1,
+        min_output_comm_size=1,
+        max_tree_size=None,
+        min_duplcheck_size=100,
+        max_repetitions=2,
+        **extract_kwargs,
+    ):
+        """
+        Initialize the Trafilatura text extraction algorithm with specified parameters.
+
+        Args:
+            required_stopword_density: Proportion of stopwords required preserve an extracted paragraph.
+                Studies on stopword lists and their distribution in various text corpora often
+                suggest that around 30-40% of a typical English text consists of stopwords.
+            min_extracted_size: Acceptable size in characters (used to trigger fallbacks).
+                Defaults to 250. See Trafilatura documentation: https://trafilatura.readthedocs.io/en/latest/settings.html.
+            min_extracted_comm_size: Works the same as min_output_comm_size for comment extraction.
+                Defaults to 1. See Trafilatura documentation: https://trafilatura.readthedocs.io/en/latest/settings.html.
+            min_output_size: Absolute acceptable minimum for main text output.
+                Defaults to 1. See Trafilatura documentation: https://trafilatura.readthedocs.io/en/latest/settings.html.
+            min_output_comm_size: Works the same as min_output_comm_size for comment extraction.
+                Defaults to 1. See Trafilatura documentation: https://trafilatura.readthedocs.io/en/latest/settings.html.
+            max_tree_size: Used to discard documents with too many elements. Defaults to None.
+            min_duplcheck_size: Minimum size in characters to run deduplication on.
+                Defaults to 100. See Trafilatura documentation: https://trafilatura.readthedocs.io/en/latest/settings.html.
+            max_repetitions: Maximum number of duplicates allowed.
+                Defaults to 2. See Trafilatura documentation: https://trafilatura.readthedocs.io/en/latest/settings.html.
+            extract_kwargs: Additional keyword arguments for the Trafilatura extract function.
+                See API documentation https://trafilatura.readthedocs.io/en/latest/usage-python.html#choice-of-html-elements
+                for list of possible parameters.
+
+        """
+        self.required_stopword_density = required_stopword_density
+        self.min_extracted_size = min_extracted_size
+        self.min_extracted_comm_size = min_extracted_comm_size
+        self.min_output_size = min_output_size
+        self.min_output_comm_size = min_output_comm_size
+        self.max_tree_size = max_tree_size
+        self.min_duplcheck_size = min_duplcheck_size
+        self.max_repetitions = max_repetitions
+        self.extract_kwargs = extract_kwargs
+
+    def extract_text(self, html, stop_words):
+        trafilatura_config = deepcopy(TRAFILATURA_DEFAULT_CONFIG)
+        trafilatura_config["DEFAULT"]["MIN_EXTRACTED_SIZE"] = str(
+            self.min_extracted_size
+        )
+        trafilatura_config["DEFAULT"]["MIN_EXTRACTED_COMM_SIZE"] = str(
+            self.min_extracted_comm_size
+        )
+        trafilatura_config["DEFAULT"]["MIN_OUTPUT_SIZE"] = str(self.min_output_size)
+        trafilatura_config["DEFAULT"]["MIN_OUTPUT_COMM_SIZE"] = str(
+            self.min_output_comm_size
+        )
+        if self.max_tree_size:
+            trafilatura_config["DEFAULT"]["MAX_TREE_SIZE"] = str(self.max_tree_size)
+        trafilatura_config["DEFAULT"]["MIN_DUPLCHECK_SIZE"] = str(
+            self.min_duplcheck_size
+        )
+        trafilatura_config["DEFAULT"]["MAX_REPETITIONS"] = str(self.max_repetitions)
+
+        text = extract_with_trafilatura(
+            html, config=trafilatura_config, **self.extract_kwargs
+        )
+
+        if text is not None:
+            paragraphs = list(filter(None, text.split("\n")))
+            result = []
+            for paragraph in paragraphs:
+                words = paragraph.split()
+                length = len(words)
+                if length == 0:
+                    continue
+                stopwords = [word for word in words if word in stop_words]
+                stopword_density = len(stopwords) / length
+
+                if stopword_density >= self.required_stopword_density:
+                    result.append(paragraph)
+        else:
+            return None
+
+        if len(result) == 0:
+            return None
+        return result
+
+
 def get_stop_list_dict(languages=[]):
 
     # Name mapping for language names from CLD2 (values)
@@ -387,7 +482,8 @@ def download_common_crawl(
       end_snapshot (str): Identifier for the latest snapshot to process, which must be chronologically after start_snapshot.
       output_type (Literal["jsonl", "parquet"]): The file format for the extracted output. Must be either "jsonl" or "parquet".
           • This is not used for the output file, but is used to check if an extracted output already exists.
-      algorithm: The text extraction algorithm instance (e.g., JusTextExtractor or ResiliparseExtractor) to use for HTML processing.
+      algorithm: The text extraction algorithm instance to use for HTML processing.
+          • This can be a JusTextExtractor (default), ResiliparseExtractor, or TrafilaturaExtractor object.
       news (bool): When True, indicates that URLs should be retrieved from the CC-NEWS dataset.
           • This also means snapshot identifiers should follow the 'YYYY-MM' format.
       aws (bool): If True, downloads are sourced from Common Crawl's S3 bucket using s5cmd;

pyproject.toml

@@ -66,6 +66,7 @@ dependencies = [
     "resiliparse",
     "sentencepiece",
     "spacy>=3.6.0, <3.8.0",
+    "trafilatura",
     "transformers>=4.48.0",
     "unidic-lite==1.0.8",
     "usaddress==0.5.10",

tests/test_download.py

@@ -19,7 +19,11 @@
 
 import pytest
 
-from nemo_curator.download import ResiliparseExtractor, download_and_extract
+from nemo_curator.download import (
+    ResiliparseExtractor,
+    TrafilaturaExtractor,
+    download_and_extract,
+)
 from nemo_curator.download.arxiv import ArxivDownloader, ArxivExtractor, ArxivIterator
 from nemo_curator.download.commoncrawl import (
     CommonCrawlWARCDownloader,
@@ -54,68 +58,73 @@ def fake_run_success(cmd, stdout, stderr):
     return FakeCompletedProcess()
 
 
+@pytest.fixture
+def html_string():
+    # Modified from https://github.com/chatnoir-eu/chatnoir-resiliparse/blob/abdf1966fb3cefe3e0790e510ab5cb1446f99a79/tests/resiliparse/extract/test_html2text.py
+    html = """<!doctype html>
+        <head>
+            <title>My Title</title>
+            <meta charset="utf-8">
+            <style>* { margin: 0; }</style>
+        </head>
+        <body>
+            <section id="wrapper">
+                <nav>
+                    <ul>
+                        <li>Nav 1</li>
+                        <li>
+                            <p>Nav 2</p>
+                            <ul>
+                                <li><p>Nav 3</p></li>
+                            </ul>
+                        </li>
+                    </ul>
+                </nav>
+                <main>
+                    This is a sample paragraph. In it we write words.
+                    These are stopwords: because did than has near we almost while what still.
+                    <a href="#foo" hidden>bar</a>
+
+                    <p>
+                    This paragraph doesn't have many stopwords. Remove it.
+                    <br>Let's keep this paragraph: either came does last new took taken making became from.
+                    </p>
+
+                    <button aria-hidden="true">Click here</button>
+                    <input type="hidden" value="foo">
+                    <input type="text" value="Some text" placeholder="Insert text">
+                    <input type="text" placeholder="Insert text">
+                    <img src="" alt="Some image">
+                    <object data="" class="some-class hidden">Cannot display object</object>
+                </main>
+                <script language="vbscript" type="text/vbscript">MsgBox("Hello World!")</script>
+                <noscript>Sorry, your browser doesn't support VB Script!</noscript>
+                <div><div><div><footer id="global-footer">
+                    Copyright (C) 2021 Foo Bar
+                </footer></div></div></div>
+            </section>
+        </body>
+    </html>"""
+    return html
+
+
 class TestDownload:
     def test_imports(self):
         from nemo_curator.download import (
             JusTextExtractor,
             ResiliparseExtractor,
+            TrafilaturaExtractor,
             download_arxiv,
             download_common_crawl,
             download_wikipedia,
         )
 
         assert True
 
-    def test_resiliparse_extract_text(self):
-        # Modified from https://github.com/chatnoir-eu/chatnoir-resiliparse/blob/abdf1966fb3cefe3e0790e510ab5cb1446f99a79/tests/resiliparse/extract/test_html2text.py
-        html = """<!doctype html>
-            <head>
-                <title>My Title</title>
-                <meta charset="utf-8">
-                <style>* { margin: 0; }</style>
-            </head>
-            <body>
-                <section id="wrapper">
-                    <nav>
-                        <ul>
-                            <li>Nav 1</li>
-                            <li>
-                                <p>Nav 2</p>
-                                <ul>
-                                    <li><p>Nav 3</p></li>
-                                </ul>
-                            </li>
-                        </ul>
-                    </nav>
-                    <main>
-                        This is a sample paragraph. In it we write words.
-                        These are stopwords: because did than has near we almost while what still.
-                        <a href="#foo" hidden>bar</a>
-
-                        <p>
-                        This paragraph doesn't have many stopwords. Remove it.
-                        <br>Let's keep this paragraph: either came does last new took taken making became from.
-                        </p>
-
-                        <button aria-hidden="true">Click here</button>
-                        <input type="hidden" value="foo">
-                        <input type="text" value="Some text" placeholder="Insert text">
-                        <input type="text" placeholder="Insert text">
-                        <img src="" alt="Some image">
-                        <object data="" class="some-class hidden">Cannot display object</object>
-                    </main>
-                    <script language="vbscript" type="text/vbscript">MsgBox("Hello World!")</script>
-                    <noscript>Sorry, your browser doesn't support VB Script!</noscript>
-                    <div><div><div><footer id="global-footer">
-                        Copyright (C) 2021 Foo Bar
-                    </footer></div></div></div>
-                </section>
-            </body>
-        </html>"""
-
+    def test_resiliparse_extract_text(self, html_string):
         algorithm = ResiliparseExtractor()
         stop_words = get_stop_list_dict()
-        result = algorithm.extract_text(html, stop_words["ENGLISH"])
+        result = algorithm.extract_text(html_string, stop_words["ENGLISH"])
 
         expected = [
             "This is a sample paragraph. In it we write words. These are stopwords: because did than has near we almost while what still.",
@@ -124,6 +133,22 @@ def test_resiliparse_extract_text(self):
 
         assert result == expected
 
+    def test_trafilatura_extract_text(self, html_string):
+        algorithm = TrafilaturaExtractor(
+            min_extracted_size=10,
+            min_duplcheck_size=10,
+            max_repetitions=1,
+            deduplicate=True,
+        )
+        stop_words = get_stop_list_dict()
+        result = algorithm.extract_text(html_string, stop_words["ENGLISH"])
+
+        expected = [
+            "Let's keep this paragraph: either came does last new took taken making became from.",
+        ]
+
+        assert result == expected
+
     def test_incorrect_snapshot_order(self):
         with pytest.raises(ValueError):
             end_snapshot = "2021-04"