Update README.md

README.md

@@ -28,3 +28,113 @@ configs:
   - split: test
     path: data/test-*
 ---
+# structured_paper_summarization
+
+A **151 k‑example** dataset of chat‐style *prompt → structured abstract* pairs, built from ~19 000 research papers across business, management, information‑systems and social‑science domains.  Each example shows the full paper (body text) being summarised into a five‑section Emerald‑style structured abstract (Purpose, Design/methodology/approach, Findings, Practical implications, Originality/value).
+
+---
+## Why this dataset?
+Large‑language models (LLMs) frequently struggle to:
+1. **Condense long scientific prose** into factual, concise summaries.
+2. **Follow rigid output structures** (e.g. subsection headings).
+
+This dataset targets both challenges simultaneously, enabling fine‑tuning or instruction‑tuning of LLMs that must output *structured* scholarly abstracts.
+
+---
+## At a glance
+| Split | Rows | Size (compressed) |
+|-------|------|-------------------|
+| train | **145 067** | 626 MB |
+| test  | **  6 650** |  29 MB |
+| **Total** | **151 717** | ≈655 MB |
+
+<sup>Counts taken from the Hugging Face viewer on 2025‑04‑29.</sup>
+
+---
+## Data schema
+```text
+{
+  title:    string                    # Paper title
+  keywords: list[string]             # Author‑supplied keywords (0‑23)
+  messages: list[dict] length ≥ 2    # ChatML‑style conversation
+}
+```
+### `messages` format
+Each list contains alternating dictionaries with:
+- `role`: either `"user"` or `"assistant"`.
+- `content`: UTF‑8 text.
+
+Typical pattern (2 items):
+```jsonc
+[
+  {
+    "role": "user",
+    "content": "Summarize the following paper into structured abstract.\n\n<full paper text>"
+  },
+  {
+    "role": "assistant",
+    "content": "Purpose: …\nDesign/methodology/approach: …\nFindings: …\nPractical implications: …\nOriginality/value: …"
+  }
+]
+```
+Some papers are longer and may be truncated to ~8 k tokens.
+
+---
+## Loading the data
+```python
+from datasets import load_dataset
+
+ds_train = load_dataset(
+    "Neooooo/19kPaper_sum_structured_prompts", split="train"
+)
+print(ds_train[0]["messages"][1]["content"][:500])
+```
+The dataset is stored as Apache **Parquet** with streaming support; the example above requires ~5 s to start iterating with no local download.
+
+---
+## Suggested use‑cases
+* **Instruction‑tuning** chat LLMs for long‑document summarisation.
+* Research on **controlled text generation** and output formatting.
+* Training **retrieval‑augmented systems** that must cite sections of the source paper.
+
+---
+## Source & construction
+1. Full‑text articles were collected via institutional access to the *Emerald Insight* corpus (open‑access + subscription).
+2. The canonical *structured abstract* supplied by each journal was extracted as ground truth.
+3. The article’s main body was embedded into a prompt of the form shown above.
+4. Data were converted to Hugging Face `datasets` ➜ auto‑parquet.
+
+No additional manual cleaning was performed; typos and OCR artefacts may persist.
+
+---
+## Licensing & acceptable use
+The article texts are **copyright their original publishers/authors** and are redistributed here *solely for non‑commercial research*.  By using this dataset you agree to:
+- **Not** redistribute the raw paper texts.
+- Cite the original articles in any derivative work.
+- Abide by Emerald’s usage policy and your local copyright laws.
+
+The **metadata & structured abstracts** are released under **CC BY‑NC 4.0**.  For commercial licensing, please contact the original rights‑holders.
+
+---
+## Citation
+If you use this dataset, please cite:
+```text
+@dataset{hu_2025_structured_prompts,
+  author       = {Xingyu Hu},
+  title        = {19kPaper\_sum\_structured\_prompts},
+  year         = 2025,
+  url          = {https://huggingface.co/datasets/Neooooo/19kPaper_sum_structured_prompts},
+  note         = {Version 1.0}
+}
+```
+
+---
+## Contributions
+Feel free to open PRs to:
+- Fix metadata errors.
+- Provide additional splits (validation, domain‑specific subsets).
+- Add scripts for evaluation or preprocessing.
+
+---
+*Happy summarising!*
+