Link paper, add text-retrieval task category, and add sample usage

README.md

@@ -1,65 +1,35 @@
 ---
+language:
+- en
 license: mit
+size_categories:
+- 10K<n<100K
 task_categories:
 - text-generation
 - text-retrieval
-language:
-- en
+pretty_name: RWGBench
 tags:
-- related-work-generation
-- scholarly-positioning
-- citation-evaluation
-- retrieval-augmented-generation
 - code
-pretty_name: RWGBench
-size_categories:
-- 10K<n<100K
 ---
 
-# RWGBench
-
-RWGBench is a benchmark for evaluating related work generation as a
-citation-centric scholarly positioning task. It tests whether a system can
-select, organize, and frame prior work for a target paper, rather than only
-producing fluent text that resembles a reference related work section.
-
-It is presented in the paper
-[RWGBench: Evaluating Scholarly Positioning in Related Work Generation](https://huggingface.co/papers/2606.24894).
-
-The official repository containing evaluation scripts and baselines is
-available on GitHub at [BFTree/RWGBench](https://github.com/BFTree/RWGBench).
+RWGBench is a benchmark for evaluating related work generation (RWG) through the lens of **scholarly positioning** and citation decision-making, rather than surface-level text similarity. It is presented in the paper [RWGBench: Evaluating Scholarly Positioning in Related Work Generation](https://huggingface.co/papers/2606.24894).
 
-## Files
+The official repository containing evaluation scripts and baselines is available on GitHub at [BFTree/RWGBench](https://github.com/BFTree/RWGBench).
 
-| File | Entries | Description |
-|---|---:|---|
-| `papers.json` | 40,108 | Source paper collection with parsed metadata, sections, related work text, and citation identifiers. |
-| `corpus.json` | 1,091,394 | Retrieval corpus. Each entry contains `doc_id`, `title`, and `abstract`. |
-| `gold100_papers.json` | 100 | Peer-reviewed evaluation split used for the main experiments. The papers are matched to accepted ICLR, NeurIPS, or ICML records. |
+It includes a large-scale paper collection, a 1,091,394 retrieval corpus, 40,108 papers in computer science with full text and citation lists, a curated 100-paper test set and a fully automated evaluation framework.
 
-## Evaluation Split Composition
+## Dataset Structure
 
-The `gold100_papers.json` split contains 100 peer-reviewed papers matched by
-exact normalized title to accepted OpenReview records. Its venue distribution
-is:
+| File                  | # Entries | Description                                                  |
+| --------------------- | --------: | ------------------------------------------------------------ |
+| `papers.json`         |    40,108 | CS papers (arXiv 2020–2025) with full text and citation lists |
+| `corpus.json`         | 1,091,394 | Retrieval corpus — title + abstract per paper                |
+| `gold100_papers.json` |       100 | Quality-filtered test set with gold related work sections    |
 
-| Venue | Papers |
-|---|---:|
-| ICLR | 42 |
-| NeurIPS | 34 |
-| ICML | 24 |
+<details>
+<summary>corpus.json &nbsp;—&nbsp; retrieval candidates</summary>
+<br>
 
-The publication-year distribution is:
-
-| Year | Papers |
-|---|---:|
-| 2023 | 2 |
-| 2024 | 61 |
-| 2025 | 37 |
-
-## Schema
-
-### `corpus.json`
 
 ```json
 {
@@ -69,7 +39,12 @@ The publication-year distribution is:
 }
 ```
 
-### `gold100_papers.json`
+</details>
+
+<details>
+<summary>gold100_papers.json &nbsp;—&nbsp; test papers</summary>
+<br>
+
 
 ```json
 {
@@ -78,40 +53,17 @@ The publication-year distribution is:
   "abstract": "...",
   "introduction": "...",
   "related_work": "Model quantization. Quantization is a widely employed technique...",
-  "citations": [191955, 118706, 517176, 264652, 1589],
-  "peer_review": {
-    "match_type": "normalized_title_exact",
-    "primary_venue": {
-      "venue": "ICLR",
-      "year": "2024",
-      "venue_id": "ICLR.cc/2024/Conference",
-      "openreview_id": "UmMa3UNDAz"
-    }
-  }
+  "citations": [191955, 118706, 517176, 264652, 1589, 2253, ... ],
+  "overall_score": 90.6
 }
 ```
 
-`citations` contains `doc_id` values from `corpus.json`. The reference related
-work section is author-written text from the target paper. Venue metadata is
-stored under `peer_review.primary_venue`.
-
-## Use With The Code Repository
-
-Download the dataset files into the GitHub repository's `data/` directory:
-
-```text
-data/
-  papers.json
-  corpus.json
-  gold100_papers.json
-```
-
-Then run the generation and evaluation scripts from the code repository.
+`citations` is a list of `doc_id`s from `corpus.json`. `overall_score` is a GLM-4 quality rating (0–100).
+</details>
 
-### Sample Usage
+## Sample Usage
 
-For integration into custom pipelines using the evaluation code from the
-GitHub repository:
+For integration into custom pipelines using the evaluation code from the GitHub repository:
 
 ```python
 from src.evaluation.single_paper_evaluator import SinglePaperEvaluator
@@ -126,15 +78,11 @@ evaluator = SinglePaperEvaluator(
 result = evaluator.evaluate(
     paper_id=...,         # int
     generated_text=...,   # str, text with [1], [2], ... citations
-    citation_list=[...],  # list of doc_id ints or title strings; index i maps to citation [i+1]
+    citation_list=[...],  # list of doc_id (int) or title (str); index i → [i+1]
 )
 ```
 
-## Data Collection And Use
-
-RWGBench is built from public scholarly documents and metadata. Source
-documents may have heterogeneous licenses, so users should follow the license
-terms of the underlying papers when redistributing document-derived text.
-
-The benchmark is intended for research on retrieval-augmented generation,
-citation selection, scholarly writing evaluation, and related work generation.
+## Data Collection & Ethics
+- **Source**: Public arXiv metadata (2020–2025), respecting arXiv's [terms of use](https://arxiv.org/help/api/tou)
+- **Copyright**: Only metadata (titles, abstracts) and author-written content; no full-text.
+- **Ethical Use**: Intended for non-commercial research only