AMEX AI-Labs: An Investigative Study on Extractive Summarization of Financial Documents

We describe the work carried out by AMEX AI-LABS on an extractive summarization benchmark task focused on Financial Narratives Summarization (FNS). This task focuses on summarizing annual financial reports which poses two main challenges as compared to typical news document summarization tasks : i) annual reports are more lengthier (average length about 80 pages) as compared to typical news documents, and ii) annual reports are more loosely structured e.g. comprising of tables, charts, textual data and images, which makes it challenging to effectively summarize. To address this summarization task we investigate a range of unsupervised, supervised and ensemble based techniques. We find that ensemble based techniques perform relatively better as compared to using only the unsupervised and supervised based techniques. Our ensemble based model achieved the highest rank of 9 out of 31 systems submitted for the benchmark task based on Rouge-L evaluation metric.