Domain Summarisation

In the landscape of document intelligence, the ability to extract precise, concise, and valuable information from comprehensive documents is paramount. With a variety of summarization methods at our disposal, the challenge often lies in choosing the right technique that provides the most coherent, relevant, and useful summary for a given use case. This is where KAPTO's approach to domain summarization distinguishes itself, by innovatively combining two well-known methods: extractive and abstractive summarization, while ensuring domain-specific entities are accurately represented.

Understanding Extractive and Abstractive Summarization

Extractive summarization operates like a binary classifier for each sentence in a document, deciding whether to include that sentence in the summary or not. It focuses on selecting near-exact sentences from the source material, meaning the resulting summary is often a direct extraction of sentences from the original text.

On the other hand, abstractive summarization adopts a more paraphrasing approach. It takes into account all the information in the input text and aims to generate a concise and coherent summary. Unlike extractive summarization, it does not rely on exact sentences from the original text, but rather presents an abstract representation of the complete information. These models are typically fine-tuned to handle specific domains, such as research papers, financial reports, or legal documents.

The KAPTO Approach

In the context of complex documents, abstractive summarization generally produces superior results due to its ability to encapsulate the entire document's content cohesively. However, there's a caveat when it comes to documents where certain entities must be present in the summary, as is often the case with legal documents. While the generative nature of abstractive summarization cannot guarantee the inclusion of these entities, extractive summarization may result in a collection of sentences lacking a clear, logical structure.

To tackle this, KAPTO has adopted a novel approach. We've integrated AI models that blend the capabilities of entity mapping and recognition with abstractive summarization, all within a reinforcement learning framework. This approach ensures that our domain-specific models harness the generative capabilities of abstractive summarization while always including vital information in the final summary.

Why are Summaries Essential to Document Intelligence?

The profound automation enabled by KAPTO's document intelligence can sometimes pose a challenge: who is keeping track of the document content when the need for human intervention is significantly reduced? For instance, in an insurance legal workflow process, general knowledge about the ongoing judicial processes might get lost once the document recognition and pairing with the claim are deeply automated.

KAPTO addresses this concern by creating expert process agents that can provide a succinct yet focused summary of each document's content. This feature is especially crucial when it comes to understanding the status of each claim that is tied to a legal process. Thus, the significance of our entity-constrained abstractive summarization becomes clear. By ensuring that all summaries are accurate, focused, and include all the essential entities, we can ensure that no crucial information is lost, even in highly automated processes.