langchain/docs/use_cases/summarization.md

# Summarization

Summarization involves creating a smaller summary of multiple longer documents.
This can be useful for distilling long documents into the core pieces of information.

The recommended way to get started using a summarization chain is:

```python
from langchain.chains.summarize import load_summarize_chain
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)
```

The following resources exist:
- [Summarization Notebook](/modules/chains/combine_docs_examples/summarize.ipynb): A notebook walking through how to accomplish this task.

Additional related resources include:
- [Utilities for working with Documents](/modules/utils/how_to_guides.rst): Guides on how to use several of the utilities which will prove helpful for this task, including Text Splitters (for splitting up long documents).
- [CombineDocuments Chains](/modules/chains/combine_docs.md): A conceptual overview of specific types of chains by which you can accomplish this task.
- [Data Augmented Generation](combine_docs.md): An overview of data augmented generation, which is the general concept of combining external data with LLMs (of which this is a subset).
Docs refactor (#480) Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com> 2 years ago			`# Summarization`

			`Summarization involves creating a smaller summary of multiple longer documents.`
improve documentation on how to pass in custom prompts (#561) 2 years ago			`This can be useful for distilling long documents into the core pieces of information.`

			`The recommended way to get started using a summarization chain is:`

			```python
			`from langchain.chains.summarize import load_summarize_chain`
			`chain = load_summarize_chain(llm, chain_type="map_reduce")`
			`chain.run(docs)`
			```
Docs refactor (#480) Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com> 2 years ago
			`The following resources exist:`
			`- [Summarization Notebook](/modules/chains/combine_docs_examples/summarize.ipynb): A notebook walking through how to accomplish this task.`

			`Additional related resources include:`
			`- [Utilities for working with Documents](/modules/utils/how_to_guides.rst): Guides on how to use several of the utilities which will prove helpful for this task, including Text Splitters (for splitting up long documents).`
			`- [CombineDocuments Chains](/modules/chains/combine_docs.md): A conceptual overview of specific types of chains by which you can accomplish this task.`
			`- [Data Augmented Generation](combine_docs.md): An overview of data augmented generation, which is the general concept of combining external data with LLMs (of which this is a subset).`