Is there anything that AI cannot do? That seems to be the question on the minds of many. This technology is transforming the world in more ways than people even realise. From predicting what items someone may be interested in on a shopping website - to creating business templates, AI technology continues to innovate and the rise of generative AI has created even more opportunities. The first blog from this two-part series, “The Future of Generative AI in the Legal Industry,” looked at how legal is already using these tools, future possibilities, the importance of remaining transparent, ethical responsibilities, and the current state of AI regulation. This blog will focus on a tool that can greatly benefit legal professionals: AI-driven text summarisation. Read on to discover what this is and how it can support internal investigations and document review.

What is Text Summarisation?

In Wolters Kluwer’s 2023 Future Ready Lawyer Survey Report, 87 percent of lawyers surveyed said technology improved daily work. While, less than half believed they were fully leveraging their tools. Instead, 50 percent said they were in a state of transition with using legal tech and 4 percent felt they were not leveraging their tools as much as they should. For generative AI, 73 percent expected to integrate this technology within a year’s time. An example of  how the legal industry is building up tech stacks and looking for more ways to innovate, with generative AI top of list.

Enter text summarisation. This is not a new concept, but i an evolving one, it is the process of summarising longer documents. Earlier methods of text summarisation use what is in the source text to create the output. AI-driven text summarisation takes this process to new heights. This transformative tool has the power to simplify the review of complex data sets. The user inputs the data into the AI tool, which scans it and provides a more concise and accurate summary highlighting key points.

The generative AI capabilities include natural language processing and natural language generation that create new sentences in the summary going beyond the source text. The ability to better understand language and infer context is what makes the summaries consistently on-point and easy to digest. When looking to invest in an AI-driven summarisation tool, it is important to ask questions about controls in place to preserve ethical walls that reduce risk and AI hallucinations. 

Two areas where generative AI text summarisation can be transformative for legal are internal investigations and document review.

Use Case: Internal Investigations

Internal investigations can stem from a range of situations carrying varying degrees of risk  from internal harassment claims to regulatory inquiries-anywhere future litigation may loom. Legal teams handling these matters need to identify core issues, relevant evidence, and potential risks. However, while thoroughness is needed, it must be quick and ideally inexpensive as internal investigations typically have smaller budgets and shorter timelines than standard discovery efforts.

An important part of the process is taking a deep dive into where the potentially relevant data that needs to be collected and reviewed resides. Text summarisation can help navigate internal investigations by overcoming challenges associated with sifting through large volumes of text. For example,- an harassment case arises and a tonne of communication over several platforms needs to be reviewed and summarised to determine whether the claim has merit and gauge its severity. A text summarisation tool with AI features can quickly sort through all the data, pinpoint communication indicative of harassment, and generate an easily digestible summary. Having this feature available can revolutionise internal investigations by providing efficient synopses that help teams quickly determine merit and jumpstart risk mitigation efforts, if necessary.

Use Case: Document Review

Text summarisation can also be a useful tool for tackling large data sets in a document review. It is often challenging to deal with the massive amounts of documents associated with litigation or regulatory matters. Legal teams need partners with the tools and expertise to imagine innovative approaches that overcome such obstacles. Remember that scalability is important , as each document review is different and will require varying degrees of technological assistance.

Using text summarisation in conjunction with other tools like AI privilege detection is one avenue to explore. In the vast sea of documents, identifying privileged information manually can be like finding a needle in a haystack. Automated privilege detection, combined with text summarisation, could be the future of efficient and compliant document review.

Picture this. An organisation is dealing with a high-stakes whistleblower case involving allegations of financial misconduct. The legal team combines AI-driven text summarisation and automated privilege detection to sift through 500,000 documents swiftly. Within a matter of hours, the tool identifies 2,000 documents that could be relevant to the case. Having these capabilities is a game changer for many reasons such as time saving, efficiency, cost control, and remaining compliant. By proactively imagining the scenarios, AI tools like this can be helpful, organisations can fine-tune their processes around document review and be better equipped when faced with complex data sets to review.

Conclusion

AI-driven text summarisation is another tool that can make legal processes more efficient and compliant. There are broader applications available in addition to the two use cases discussed that will continue to unfold. Talking to industry contacts and providers about new capabilities, the world of generative AI should be a top priority for legal professionals. This will help maintain awareness and make connections toassist with legal tech investments and strategy.