---
title: "Document Summarizer"
description: "Free document summarizer. Upload a PDF or text file and get a TLDR with key points. The pipeline extracts text and summarizes it with an LLM, all in a Pixeltable table."
keywords:
  - summarize pdf
  - document summarizer
  - pdf summarizer free
  - summarize document online
  - tldr generator
url: "https://pixeltable.com/tools/document-summarizer"
---

# Document Summarizer

Free document summarizer. Upload a PDF or text file and get a TLDR with key points. The pipeline extracts text and summarizes it with an LLM, all in a Pixeltable table.

Free online tool by Pixeltable. Run it at https://pixeltable.com/tools/document-summarizer (interactive, no sign-up).

## Features

- TLDR with key points
- PDF and text
- No sign-up
- Free

## FAQ

### How long can the document be?

Most documents work well; very long files are truncated to fit the model context.

### Is it free?

Yes, completely free with no sign-up required.


## Fact sheet

Last updated 2026-09-23.

| Fact | Value |
| --- | --- |
| Modality | Document |
| Inputs | PDF or text file |
| Outputs | Summary text |
| Computed column | extract_document_text, then Groq chat_completions for the summary |
| What updates | A new or changed row recomputes only that row. Unchanged rows stay cached. |
| Canonical doc | [Computed columns](https://docs.pixeltable.com/datastore/computed-columns) |

```python
import pixeltable as pxt
from pixeltable.functions.groq import chat_completions

TableModel = pxt.model_base()

class DocChat(TableModel, name='doc_chat'):
    document: pxt.Document
    question: pxt.String

    extracted_text = extract_document_text(document)
    llm_prompt = build_doc_prompt(extracted_text, question)
    answer = chat_completions(
        messages=[{'role': 'user', 'content': llm_prompt}],
        model='llama-3.3-70b-versatile',
    ).choices[0].message.content
```