Update README.md

README.md

@@ -50,16 +50,13 @@ the full field-level specification. Key fields:
 - `websci_meta.sci_quality_score`: 0–5 scientific value score from the
   rubric-based quality classifier.
 
-## Curation Summary
+## Data Curation Pipeline
+The HainaWeb-Sci corpus is constructed from DCLM-Pool and DCLM-Pool-aligned Common Crawl data (2013–March 2026). We process the raw data through a four-stage data-centric curation pipeline tailored to scientific web data:
 
-Documents are sourced from Common Crawl snapshots (2013–March 2026) and
-DCLM-Pool, then processed by the data-centric curation pipeline described
-in Section 3 of the paper:
-
-1. Heuristic filtering with discipline-aware differential rules.
-2. Two-tier content-driven discipline routing.
-3. Rubric-based quality scoring with asymmetric penalties.
-4. Intra-dump fuzzy + intra-disciplinary exact deduplication.
+- Stage 1 (Data Preparation) transforms raw web data into a consistent textual foundation through WARC parsing, text extraction, URL filtering, and language identification. 
+- Stage 2 (Quality Filtering) focuses on science-aware content refinement by combining rule-based and model-based filtering. Unlike general-purpose pipelines, both heuristic rules and learned quality models are customized to better preserve scientifically meaningful content, particularly symbolic expressions and structured reasoning.
+- Stage 3 (Deduplication) removes redundant content at scale to improve corpus diversity and training efficiency. 
+- Stage 4 (Discipline-Specific Curation) introduces fine-grained discipline classification and scientific value scoring, enabling structured organization of scientific knowledge across 14 disciplines and providing quantitative signals for data selection.
 
 ## PII Handling