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typo_summarization_tutorial (#25938)

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- [ ] **PR title**:
docs: fix typo in summarization_tutorial


- [ ] **PR message**: 
docs: fix couple of typos in summarization_tutorial

- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
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4
docs/docs/tutorials/summarization.ipynb
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@ -130,7 +130,7 @@
"\n",
"2. `Map-reduce`: Summarize each document on its own in a \"map\" step and then \"reduce\" the summaries into a final summary (see [here](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain.html) for more on the `MapReduceDocumentsChain`, which is used for this method).\n",
"\n",
"Note that map-reduce is especially effective when understanding of a sub-document does not rely on preceeding context. For example, when summarizing a corpus of many, shorter documents. In other cases, such as summarizing a novel or body of text with an inherent sequence, [iterative refinement](/docs/how_to/summarize_refine) may be more effective."
"Note that map-reduce is especially effective when understanding of a sub-document does not rely on preceding context. For example, when summarizing a corpus of many, shorter documents. In other cases, such as summarizing a novel or body of text with an inherent sequence, [iterative refinement](/docs/how_to/summarize_refine) may be more effective."
]
},
{
@ -346,7 +346,7 @@
"\n",
"Note that the map step is typically parallelized over the input documents.\n",
"\n",
"[LangGraph](https://langchain-ai.github.io/langgraph/), built on top of `langchain-core`, suports [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) workflows and is well-suited to this problem:\n",
"[LangGraph](https://langchain-ai.github.io/langgraph/), built on top of `langchain-core`, supports [map-reduce](https://langchain-ai.github.io/langgraph/how-tos/map-reduce/) workflows and is well-suited to this problem:\n",
"\n",
"- LangGraph allows for individual steps (such as successive summarizations) to be streamed, allowing for greater control of execution;\n",
"- LangGraph's [checkpointing](https://langchain-ai.github.io/langgraph/how-tos/persistence/) supports error recovery, extending with human-in-the-loop workflows, and easier incorporation into conversational applications.\n",