Key-phrase boosted unsupervised summary generation for FinTech
  organization
Aadit Deshpande Shreya Goyal Prateek Nagwanshi Avinash Tripathy
With the recent advances in social media, the use of NLP techniques in social
media data analysis has become an emerging research direction. Business
organizations can particularly benefit from such an analysis of social media
discourse, providing an external perspective on consumer behavior. Some of the
NLP applications such as intent detection, sentiment classification, text
summarization can help FinTech organizations to utilize the social media
language data to find useful external insights and can be further utilized for
downstream NLP tasks. Particularly, a summary which highlights the intents and
sentiments of the users can be very useful for these organizations to get an
external perspective. This external perspective can help organizations to
better manage their products, offers, promotional campaigns, etc. However,
certain challenges, such as a lack of labeled domain-specific datasets impede
further exploration of these tasks in the FinTech domain. To overcome these
challenges, we design an unsupervised phrase-based summary generation from
social media data, using 'Action-Object' pairs (intent phrases). We evaluated
the proposed method with other key-phrase based summary generation methods in
the direction of contextual information of various Reddit discussion threads,
available in the different summaries. We introduce certain "Context Metrics"
such as the number of Unique words, Action-Object pairs, and Noun chunks to
evaluate the contextual information retrieved from the source text in these
phrase-based summaries. We demonstrate that our methods significantly
outperform the baseline on these metrics, thus providing a qualitative and
quantitative measure of their efficacy. Proposed framework has been leveraged
as a web utility portal hosted within Amex.