Update dataset card with paper, project, and code links

README.md

@@ -1,12 +1,12 @@
 ---
-license: odc-by
-task_categories:
-- question-answering
 language:
 - en
-pretty_name: ASTRA-QA
+license: odc-by
 size_categories:
 - 1K<n<10K
+task_categories:
+- question-answering
+pretty_name: ASTRA-QA
 configs:
 - config_name: questions
   default: true
@@ -21,41 +21,53 @@ configs:
 
 # ASTRA-QA: A Benchmark for Abstract Question Answering over Documents
 
-ASTRA-QA, short for AbSTRAct Question Answering over documents, is a dataset and benchmark for document-level, synthesis-heavy question answering in retrieval-augmented generation systems.
-It evaluates whether a system can read long documents, organize evidence, and produce grounded abstractive answers with reference-based assessment, rather than only retrieve short facts.
+[**Paper**](https://huggingface.co/papers/2605.10168) | [**Project Page**](https://xinyangsally.github.io/astra-benchmark) | [**GitHub**](https://github.com/xiaojingang12/AURA)
+
+ASTRA-QA (AbSTRAct Question Answering over documents) is a dataset and benchmark for document-level, synthesis-heavy question answering in retrieval-augmented generation (RAG) systems. It evaluates whether a system can read long documents, organize evidence, and produce grounded abstractive answers using a reference-based assessment paradigm.
 
 ## Dataset Summary
 
-- **869 questions** in total
-- **5 task types**: `Single-Sum`, `Pair-Comp`, `Multi-Comp`, `Enum`, and `Temp`
-- **2 source domains**: academic documents and news documents
-- **3 retrieval scopes** used in the benchmark: `Simple`, `Middle`, and `Hard`
+- **869 questions** in total.
+- **5 task families**: `Single-Sum`, `Pair-Comp`, `Multi-Comp`, `Enum`, and `Temp`.
+- **2 source domains**: Academic papers (arXiv, OpenReview) and news documents.
+- **3 retrieval scopes**: `Simple`, `Middle`, and `Hard` (distractor-heavy).
 
 ## Dataset Files
 
-- `corpus.jsonl`: source documents used for retrieval and evidence grounding
-- `questions.jsonl`: abstractive question-answer pairs with topic-set style answers and benchmark metadata
+- `corpus.jsonl`: Source documents used for retrieval and evidence grounding.
+- `questions.jsonl`: Abstractive question-answer pairs with topic-set style answers, aligned evidence, and benchmark metadata.
 
 ## Task Types
 
-- **Single-Sum**: summarize a single document into a compact grounded answer
-- **Pair-Comp**: compare two documents, methods, entities, or events
-- **Multi-Comp**: synthesize comparisons across multiple targets
-- **Enum**: enumerate key items, themes, findings, or contributions
-- **Temp**: reconstruct temporally evolving events over a time window
+- **Single-Sum**: Summarize a single document into a compact grounded answer.
+- **Pair-Comp**: Compare two documents, methods, entities, or events.
+- **Multi-Comp**: Synthesize comparisons across multiple targets.
+- **Enum**: Enumerate key items, themes, findings, or contributions.
+- **Temp**: Reconstruct temporally evolving events over a time window.
+
+## Reference-Based Evaluation
+
+Unlike traditional QA benchmarks that often rely on short facts or pairwise preference judgments, ASTRA-QA uses curated **topic sets**. This allows the benchmark to assess whether a model covers required key points and avoids unsupported content (hallucination) by directly scoring topic coverage, enabling a more interpretable and scalable evaluation of abstractive RAG quality.
 
 ## Data Sources
 
-ASTRA-QA is constructed from publicly available sources, including arXiv, OpenReview, and news articles collected through `mediastack.com`.
+ASTRA-QA is constructed from publicly available sources, including academic papers from arXiv and OpenReview, and news articles collected through `mediastack.com`.
 
 ## License
 
-This dataset is released under the Open Data Commons Attribution License (`ODC-By`).
+This dataset is released under the **Open Data Commons Attribution License (ODC-By)**. 
 
-The `ODC-By` license applies to the dataset annotations, organization, metadata, and benchmark construction.
-Original source documents remain subject to their respective licenses and terms of use.
-Users are responsible for complying with the original licenses and source-specific usage terms when using the source content.
+The `ODC-By` license applies to the dataset annotations, organization, metadata, and benchmark construction. Original source documents remain subject to their respective licenses and terms of use. Users are responsible for complying with the original licenses and source-specific usage terms.
 
 ## Citation
 
-Citation information will be added when the paper is released.
+If you find ASTRA-QA useful, please cite the following:
+
+```bibtex
+@article{astra_qa_2026,
+  title   = {ASTRA-QA: A Benchmark for Abstract Question Answering over Documents},
+  author  = {TBD},
+  journal = {arXiv},
+  year    = {2026}
+}
+```