| --- |
| language: |
| - en |
| license: apache-2.0 |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - text-generation |
| - summarization |
| pretty_name: Summarization SFT (100K) |
| tags: |
| - summarization |
| - sft |
| - supervised-fine-tuning |
| - email |
| - meeting-notes |
| - business-documents |
| - document-understanding |
| - synthetic |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: summarization-sft-100k.jsonl |
| --- |
| |
| # Summarization SFT (100K) |
|
|
| 100,000 ShareGPT-format conversations covering document summarization across 9 source types and 5 summary styles. Trains models to summarize professional documents the way an expert human analyst would — identifying what matters, choosing the right format, and calibrating length to the task. |
|
|
| ## Motivation |
|
|
| Summarization is one of the most commercially deployed LLM capabilities, yet most summarization datasets train on news articles only. Real enterprise use cases involve email threads, meeting notes, incident reports, legal clauses, and Slack conversations — each with different structure, reading level, and output expectations. This dataset covers the full range. |
|
|
| ## Dataset Description |
|
|
| **100,000 conversations** across 9 source types and 5 summary styles: |
|
|
| ### Source Type Distribution |
|
|
| | Source Type | Count | % | |
| |---|---|---| |
| | Email thread | ~19,000 | 19% | |
| | Meeting notes | ~14,300 | 14% | |
| | News article | ~14,300 | 14% | |
| | Incident report | ~9,500 | 10% | |
| | Academic abstract | ~9,500 | 10% | |
| | Legal document | ~9,500 | 10% | |
| | Product reviews | ~9,500 | 10% | |
| | Technical report | ~9,500 | 10% | |
| | Chat conversation | ~4,800 | 5% | |
|
|
| ### Summary Style Distribution |
|
|
| | Style | Count | Description | |
| |---|---|---| |
| | `bullets` | ~38,000 | Structured bullet points with headings | |
| | `executive_summary` | ~28,600 | Paragraph form for leadership audiences | |
| | `single_sentence` | ~14,300 | One-sentence TL;DR | |
| | `action_items` | ~9,500 | Extracted action items with owners and dates | |
| | `plain_language` | ~9,500 | Technical content simplified for non-expert readers | |
|
|
| ## Format |
|
|
| ```json |
| { |
| "conversations": [ |
| { |
| "from": "human", |
| "value": "Summarize this email thread concisely:\n\n---\n**From:** Sarah Chen...\n**Subject:** Database migration status\n...\n" |
| }, |
| { |
| "from": "gpt", |
| "value": "**Email Thread Summary**\n\nThe engineering and support teams are coordinating a PostgreSQL 11→15 database migration...\n\n**Open items**:\n1. Sarah to negotiate..." |
| } |
| ], |
| "metadata": { |
| "source_type": "email_thread", |
| "summary_style": "executive_summary" |
| }, |
| "id": "abc123" |
| } |
| ``` |
|
|
| ## Key Training Signals |
|
|
| 1. **Format selection**: The response format matches the summary style requested — bullet points when asked, prose when asked, tables for structured data |
| 2. **Information priority**: Key decisions, owners, and deadlines surface first; background context is compressed |
| 3. **Action item extraction**: Responses identify responsible parties, tasks, and deadlines with high precision |
| 4. **Audience calibration**: Technical content is simplified for `plain_language` style; preserved for `bullets` and `executive_summary` |
| 5. **Length discipline**: `single_sentence` summaries are exactly one sentence; `executive_summary` runs 3–5 sentences |
|
|
| ## Source Type Details |
|
|
| - **Email threads**: Multi-party coordination, decisions, and follow-ups extracted from conversation flow |
| - **Meeting notes**: Unstructured notes reformatted into decisions, action items, and blockers |
| - **Incident reports**: Technical postmortems distilled for executive audiences and action tracking |
| - **Academic abstracts**: Research findings translated into accessible plain language |
| - **Legal documents**: Contract clauses explained without legalese |
| - **Product reviews**: Customer sentiment themes identified and structured for product teams |
| - **Technical reports**: Audit findings and recommendations structured by priority |
| - **Chat conversations**: Slack/Teams discussions condensed to decisions and next steps |
|
|
| ## Use Cases |
|
|
| - SFT fine-tuning for document summarization in enterprise applications |
| - Training models for email triage and meeting assistant products |
| - Legal tech applications requiring plain-language document summaries |
| - Customer success tools that summarize product review themes |
| - On-call tools that condense incident reports for engineering leadership |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
