Summarization of legal text is an essential part of a number of products in Thomson Reuters. The challenge is that writing summaries for multiple 10–100-page documents every day is very time consuming and laborious. By adding AI summarization capabilities to an existing product, we augmented the workflow of our editorial team – instead of writing the summaries from scratch they now review machine-generated summaries. We knew that one problem with the existing tool was that the outcomes generated by the AI summarization were not fully trusted, and so we looked at ways to improve the trust in the system. In this talk you will learn about how we added an extra layer of explainability to the machine-generated summaries, how to select suitable explainability mechanisms and how our users perceived it.
- Explainability is very important for the adoption of AI systems.
- Explainability helped the editors interacting with the AI system become even more efficient and strengthened their trust in the AI system.
- Not all explainability methods are equal in the benefits they create to users. Explainability methods should be carefully tailored to the task and the user needs!
- We will give a brief overview of the attention mechanism as one approach to add explainability.
Milda Norkute | Senior Designer | Thomson Reuters
Milda Norkute is a Senior Designer at Thomson Reuters Labs in Zug, Switzerland, one of several labs worldwide. She works closely with data scientists and engineers on enhancing products and services across Thomson Reuters product portfolio with Artificial Intelligence (AI) solutions. Milda is focused on user research and design of the products to figure out how and where to put the human in the loop in AI powered systems.Before joining Thomson Reuters Milda worked at Nokia and CERN. Milda holds a masters in Human Computer Interaction and a bachelors degree in Psychology.
Nina Hristozova | Data Scientist | Thomson Reuters
Nina is a Data Scientist at Thomson Reuters (TR) Labs. She holds a BSc in Computer Science from the University of Glasgow, Scotland. As part of her role at TR she has worked on a wide range of projects applying AI technologies to NLP problems driving innovation and incorporating a customer first mindset. Her current focus is on text summarization and information extraction.
Outside of work she continues to spread the love for NLP as a Co-organizer of the NLP Zurich Meetup. And as a hobby Nina coaches volleyball.
Day 2 | M8 | Machine And Deep Learning Stage
Explainability for Text Summarization of Legal Documents | Milda Norkute & Nina Hristozova | Senior Designer & Data Scientist | Thomson Reuters