---
dataset_info:
  features:
  - name: title
    dtype: string
  - name: keywords
    sequence: string
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  splits:
  - name: train
    num_bytes: 1851098097.8341584
    num_examples: 145064
  - name: test
    num_bytes: 78063099.39124106
    num_examples: 6653
  download_size: 626249553
  dataset_size: 1929161197.2253995
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - 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/structured_paper_summarization", 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        = {structured_paper_summarization},
  year         = 2025,
  url          = {https://huggingface.co/datasets/Neooooo/structured_paper_summarization},
  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!*