Hi, I’m Milda and I’ll walk you through our case study which assessed the usefulness and impact
of added explainability features in legal document summarization.
The background for this study was that as part of the editorial process,
using one of Thomson Reuters legal editorial tools, our editors have to
summarize allegations made by the plaintiffs based on long court case text documents.
So to support them we developed a text summarization solution.
This was a deep learning model, a pointer generator network. And from the evaluations
of the model we’ve concluded that this should be a human in the loop solution,
where the editors have to verify each summary. However, what we heard was that the editors
felt they still needed to look at an entire court case to be able to verify the summary.
So in this work we’ve researched if we could support the editors by adding explainability
features to the summary review process– namely would explainability help to reduce time spent
on validating the summary and if it could help to increase the editor’s trust in the AI system.
The two explainability methods we experimented with were attention vector approach which used
attention scores produced by the model at the time of prediction to show which parts within the
input text did the model pay attention to while generating the summary. This results in different
intensity of text highlights, where darker blue means more attention, and lighter blue means less.
We named this attention highlights. The second one was source attribution approach which is a model
independent heuristic to identify one or more sentences in the input text which the summary’s
language primarily comes from. So here the text either had influence on summary or it didn’t have
influence which is why we have only one shade of blue. And we called this source highlights.
We had two rounds of testing in total with two editors and this was a within subject
study. In round one of testing both editors had work with a web interface resembling their actual
tool with no highlights and with attention highlights. For the experiment outline,
both editors were timed in the background as they reviewed the summaries. They both
were exposed to highlights and no highlights conditions which was followed by an interview
and the key thing was that even though they were both exposed to both conditions,
they never saw the same case twice and we always had a control for a highlighted case.
In the second round of testing experiment outline was essentially the same. The editors had to worth
with no highlights and they also had to work with an interface which had source highlights.
Now for the results, we learned that the editors were significantly faster
in reviewing the summaries with attention highlights, however, the effect of source
highlights on their performance in terms of saving time, was a lot less clear.
And from the interviews we learned why.
So after working with the source highlights the editors have said do I just trust that
one section which was highlighted? It doesn’t feel like I’ve done my full job as a reviewer. However,
the attention highlights and the different shades of blue it had gave them a sense that
the system did look at the whole case. So even if it got it wrong, it made them trust it more.
Now in terms of how the highlights were used, the source highlights were primarily used as a
way to get to the area of the document where the key details relevant to the summary might be,
however, they were not always there. Whereas the attention highlights did take them to the
area of the document where the key details of the summary were and further to this it also gave them
the information which they sometimes chose to add to summary in addition to what was already there.
Furthermore, the editors have said that attention highlights one time have helped them realize
that the AI system has made some incorrect conclusions and helped them correct this.
Based on our research we’ve concluded that: explainability can create measurable
benefits in terms of saved time. Well chosen explainability method can enrich interactions
between AI system and the user. However, not all methods to enable explainability are equal
in the benefits they create. For that reason, explainability methods should be designed to task.