legal-summarization changedabout-claude/use-case-guides/legal-summarization
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11---
22title: Legal summarization
33url: https://platform.claude.com/docs/en/about-claude/use-case-guides/legal-summarization
4description: This guide walks through how to leverage Claude's advanced natural language processing capabilities to efficiently summarize legal documents, extracting key information and expediting legal research. With Claude, you can streamline the review of contracts, litigation prep, and regulatory work, saving time and ensuring accuracy in your legal processes.
4description: This guide walks through how to use Claude's advanced natural language processing capabilities to efficiently summarize legal documents, extracting key information and expediting legal research. With Claude, you can streamline the review of contracts, litigation prep, and regulatory work, saving time and ensuring accuracy in your legal processes.
55---
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7> Visit the [summarization cookbook](https://platform.claude.com/cookbook/capabilities-summarization-guide) to see an example legal summarization implementation using Claude.
7> Visit the [summarization recipe](https://platform.claude.com/cookbook/capabilities-summarization-guide) in the Claude Cookbook to see an example legal summarization implementation using Claude.
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99## Before building with Claude
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1515<AccordionGroup>
1616 <Accordion title="You want to review a high volume of documents efficiently and affordably">
17 Large-scale document review can be time-consuming and expensive when done manually. Claude can process and summarize vast amounts of legal documents rapidly, significantly reducing the time and cost associated with document review. This capability is particularly valuable for tasks like due diligence, contract analysis, or litigation discovery, where efficiency is crucial.
17 Large-scale document review can be time-consuming and expensive when done manually. Claude can process and summarize vast amounts of legal documents rapidly, significantly reducing the time and cost associated with document review. This capability is particularly valuable for tasks such as due diligence, contract analysis, or litigation discovery, where efficiency is crucial.
1818 </Accordion>
1919
2020 <Accordion title="You require automated extraction of key metadata">
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3030 </Accordion>
3131
3232 <Accordion title="You want to streamline and expedite your legal research process">
33 Claude can assist in legal research by quickly analyzing large volumes of case law, statutes, and legal commentary. It can identify relevant precedents, extract key legal principles, and summarize complex legal arguments. This capability can significantly speed up the research process, allowing legal professionals to focus on higher-level analysis and strategy development.
33 Claude can help with legal research by quickly analyzing large volumes of case law, statutes, and legal commentary. It can identify relevant precedents, extract key legal principles, and summarize complex legal arguments. This capability can significantly speed up the research process, allowing legal professionals to focus on higher-level analysis and strategy development.
3434 </Accordion>
3535</AccordionGroup>
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121121
122122### Establish success criteria
123123
124Evaluating the quality of summaries is a notoriously challenging task. Unlike many other natural language processing tasks, evaluation of summaries often lacks clear-cut, objective metrics. The process can be highly subjective, with different readers valuing different aspects of a summary. Here are criteria you may want to consider when assessing how well Claude performs legal summarization.
124Evaluating the quality of summaries is a notoriously challenging task. Unlike many other natural language processing tasks, evaluation of summaries often lacks clear-cut, objective metrics. The process can be highly subjective, with different readers valuing different aspects of a summary. Here are criteria you might want to consider when assessing how well Claude performs legal summarization.
125125
126126<AccordionGroup>
127127 <Accordion title="Factual correctness">
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157157
158158### Select the right Claude model
159159
160Model accuracy is extremely important when summarizing legal documents. Claude Opus 5.5 is an excellent choice for use cases such as this where high accuracy is required. If the size and quantity of your documents is large such that costs start to become a concern, you can also try using a smaller model such as Claude Haiku 5.5.
160Model accuracy is extremely important when summarizing legal documents. Claude Opus 5.5 is an excellent choice for use cases such as this where high accuracy is required. If the size and quantity of your documents are large such that costs start to become a concern, you can also try using a smaller model such as Claude Haiku 5.5.
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162162To help estimate these costs, the following is a comparison of the cost to summarize 1,000 sublease agreements using Opus and Haiku models:
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186186 * Total cost: $54.00 + $7.75 = $61.75 USD
187187
188188<Tip>
189 Actual costs may differ from these estimates. The token counts come from running the example in the
189 Actual costs might differ from these estimates. The token counts come from running the example in the
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191191 [Build a strong prompt](https://platform.claude.com/docs/en/about-claude/use-case-guides/legal-summarization#build-a-strong-prompt)
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194194
195195 [long-prompt prices](https://platform.claude.com/docs/en/about-claude/pricing#long-context-pricing)
196196
197 . A document whose prompt stays at or under 100,000 tokens pays $0.10/MTok input and $0.50/MTok output instead. To see which prices apply, count your documents with
197 . A document whose prompt stays at or under 100,000 tokens pays $0.10 / $0.50 USD per million input / output tokens instead. To see which prices apply, count your documents with
198198
199199 [token counting](https://platform.claude.com/docs/en/build-with-claude/token-counting)
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211211
212212### Transform documents into a format that Claude can process
213213
214Before you begin summarizing documents, you need to prepare your data. This involves extracting text from PDFs, cleaning the text, and ensuring it's ready to be processed by Claude.
214Before you start summarizing documents, you need to prepare your data. This involves extracting text from PDFs, cleaning the text, and ensuring it's ready to be processed by Claude.
215215
216216Here is a demonstration of this process on a sample PDF:
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465465 ```
466466</CodeGroup>
467467
468In this example, you first download a PDF of a sample sublease agreement used in the [summarization cookbook](https://platform.claude.com/cookbook/capabilities-summarization-guide). This agreement was sourced from a publicly available sublease agreement from the [sec.gov website](https://www.sec.gov/Archives/edgar/data/1045425/000119312507044370/dex1032.htm).
468In this example, you first download a PDF of a sample sublease agreement used in the Claude Cookbook's [summarization recipe](https://platform.claude.com/cookbook/capabilities-summarization-guide). This agreement was sourced from a publicly available sublease agreement from the [sec.gov website](https://www.sec.gov/Archives/edgar/data/1045425/000119312507044370/dex1032.htm).
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470470The example uses the `pypdf` (typescript: `pdf-parse`; csharp: `PdfPig`; go: `github.com/ledongthuc/pdf`; java: `org.apache.pdfbox:pdfbox`; php: `smalot/pdfparser`; ruby: `pdf-reader`) library to extract the contents of the PDF and convert it to text. The text data is then cleaned by removing page numbers and extra whitespace.
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836836Within the function, a prompt is generated for Claude, including the document to be summarized, the details to extract, and specific instructions for summarizing the document. The prompt instructs Claude to respond with a summary of each detail to extract nested within XML headers.
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838Because the code outputs each section of the summary within tags, each section can easily be parsed out as a post-processing step. This approach enables structured summaries that can be adapted for your use case, so that each summary follows the same pattern.
838Because the code outputs each section of the summary within tags, each section can be parsed out as a post-processing step. This approach makes it possible to create structured summaries that can be adapted for your use case, so that each summary follows the same pattern.
839839
840840### Evaluate your prompt
841841
842Prompting often requires testing and optimization for it to be production ready. To determine the readiness of your solution, evaluate the quality of your summaries using a systematic process combining quantitative and qualitative methods. Creating a [strong empirical evaluation](https://platform.claude.com/docs/en/test-and-evaluate/develop-tests#build-evaluations) based on your defined success criteria allows you to optimize your prompts. Here are some metrics you may want to include within your empirical evaluation:
842Prompting often requires testing and optimization for it to be production ready. To determine the readiness of your solution, evaluate the quality of your summaries using a systematic process combining quantitative and qualitative methods. Creating a [strong empirical evaluation](https://platform.claude.com/docs/en/test-and-evaluate/develop-tests#build-evaluations) based on your defined success criteria allows you to optimize your prompts. Here are some metrics you might want to include within your empirical evaluation:
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844844<AccordionGroup>
845845 <Accordion title="ROUGE scores">
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8748741. **Ensure no liability:** Understand the legal implications of errors in the summaries, which could lead to legal liability for your organization or clients. Provide disclaimers or legal notices clarifying that the summaries are generated by AI and should be reviewed by legal professionals.
875875
8762. **Handle diverse document types:** This guide discusses how to extract text from PDFs. In the real world, documents may come in a variety of formats (such as PDFs, Word documents, and text files). Ensure your data extraction pipeline can convert all of the file formats you expect to receive.
8762. **Handle diverse document types:** This guide discusses how to extract text from PDFs. In the real world, documents might come in a variety of formats (such as PDFs, Word documents, and text files). Ensure your data extraction pipeline can convert all of the file formats you expect to receive.
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8783. **Parallelize API calls to Claude:** Long documents with a large number of tokens may require up to a minute for Claude to generate a summary. For large document collections, you may want to send API calls to Claude in parallel so that the summaries can be completed in a reasonable timeframe. Refer to Anthropic’s [rate limits](https://platform.claude.com/docs/en/api/rate-limits#rate-limits) to determine the maximum amount of API calls that can be performed in parallel.
8783. **Parallelize API calls to Claude:** Long documents with a large number of tokens might require up to a minute for Claude to generate a summary. For large document collections, you might want to send API calls to Claude in parallel so that the summaries can be completed in a reasonable timeframe. Refer to Anthropic’s [rate limits](https://platform.claude.com/docs/en/api/rate-limits#rate-limits) to determine the maximum number of API calls that can be performed in parallel.
879879
880880***
881881
882882## Improve performance
883883
884In complex scenarios, it may be helpful to consider additional strategies to improve performance beyond standard [prompt engineering techniques](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview). Here are some advanced strategies:
884In complex scenarios, it might be helpful to consider additional strategies to improve performance beyond standard [prompt engineering techniques](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview). Here are some advanced strategies:
885885
886886### Perform meta-summarization to summarize long documents
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13321332### Use summary indexed documents to explore a large collection of documents
13331333
1334Searching a collection of documents with an LLM usually involves retrieval-augmented generation (RAG). However, in scenarios involving large documents or when precise information retrieval is crucial, a basic RAG approach may be insufficient. Summary indexed documents is an advanced RAG approach that provides a more efficient way of ranking documents for retrieval, using less context than traditional RAG methods. In this approach, you first use Claude to generate a concise summary for each document in your corpus, and then use Claude to rank the relevance of each summary to the query being asked. For further details on this approach, including a code-based example, check out the summary indexed documents section in the [summarization cookbook](https://platform.claude.com/cookbook/capabilities-summarization-guide).
1334Searching a collection of documents with an LLM usually involves retrieval-augmented generation (RAG). However, in scenarios involving large documents or when precise information retrieval is crucial, a basic RAG approach might be insufficient. Summary indexed documents is an advanced RAG approach that provides a more efficient way of ranking documents for retrieval, using less context than traditional RAG methods. In this approach, you first use Claude to generate a concise summary for each document in your corpus, and then use Claude to rank the relevance of each summary to the query being asked. For further details on this approach, including a code-based example, see the summary indexed documents section in the [summarization recipe](https://platform.claude.com/cookbook/capabilities-summarization-guide).
13351335
13361336### Fine-tune Claude to learn from your dataset
13371337
13381338Another advanced technique to improve Claude's ability to generate summaries is fine-tuning. Fine-tuning involves training Claude on a custom dataset that specifically aligns with your legal summarization needs, ensuring that Claude adapts to your use case. Here’s an overview on how to perform fine-tuning:
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13401. **Identify errors:** Start by collecting instances where Claude’s summaries fall short - this could include missing critical legal details, misunderstanding context, or using inappropriate legal terminology.
13401. **Identify errors:** Start by collecting instances where Claude’s summaries are inadequate. This could include missing critical legal details, misunderstanding context, or using inappropriate legal terminology.
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13422. **Curate a dataset:** Once you've identified these issues, compile a dataset of these problematic examples. This dataset should include the original legal documents alongside your corrected summaries, ensuring that Claude learns the desired behavior.
13422. **Curate a dataset:** Once you've identified these issues, compile a dataset of these problematic examples. This dataset should include the original legal documents alongside your corrected summaries, ensuring that Claude learns the behavior you want.
13431343
134413443. **Perform fine-tuning:** Fine-tuning involves retraining the model on your curated dataset to adjust its weights and parameters. This retraining helps Claude better adapt to the specific requirements of your legal domain, improving its ability to summarize documents according to your standards.
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13464. **Iterative improvement:** Fine-tuning is not a one-time process. As Claude continues to generate summaries, you can iteratively add new examples where it has underperformed, further refining its capabilities. Over time, this continuous feedback loop will result in a model that is highly specialized for your legal summarization tasks.
13464. **Improve iteratively:** Fine-tuning is not a one-time process. As Claude continues to generate summaries, you can iteratively add new examples where it has underperformed, further refining its capabilities. Over time, this continuous feedback loop will result in a model that is highly specialized for your legal summarization tasks.
13471347
13481348<Tip>
13491349 Fine-tuning is currently only available through Amazon Bedrock. Additional details are available in the
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13541354</Tip>
13551355
13561356<CardGroup cols={2}>
1357 <Card title="Summarization cookbook" icon="link" href="https://platform.claude.com/cookbook/capabilities-summarization-guide">
1357 <Card title="Summarization recipe" icon="link" href="https://platform.claude.com/cookbook/capabilities-summarization-guide">
13581358 View a fully implemented code-based example of how to use Claude to summarize contracts.
13591359 </Card>
13601360
1361 <Card title="Citations cookbook" icon="link" href="https://platform.claude.com/cookbook/misc-using-citations">
1362 Explore the Citations cookbook recipe for guidance on how to ensure accuracy and explainability of information.
1361 <Card title="Citations recipe" icon="link" href="https://platform.claude.com/cookbook/misc-using-citations">
1362 Explore the Citations recipe for guidance on how to ensure accuracy and explainability of information.
13631363 </Card>
13641364</CardGroup>
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