  A law practitioner has to go through numerous lengthy legal case proceedings
for their practices of various categories, such as land dispute, corruption,
etc. Hence, it is important to summarize these documents, and ensure that
summaries contain phrases with intent matching the category of the case. To the
best of our knowledge, there is no evaluation metric that evaluates a summary
based on its intent. We propose an automated intent-based summarization metric,
which shows a better agreement with human evaluation as compared to other
automated metrics like BLEU, ROUGE-L etc. in terms of human satisfaction. We
also curate a dataset by annotating intent phrases in legal documents, and show
a proof of concept as to how this system can be automated. Additionally, all
the code and data to generate reproducible results is available on Github.
