Duplicate Filtering Overview

A critical part of the analysis involves ensuring that the thesis points extracted from different financial posts are unique and not duplicates. Duplicate points—essentially similar arguments or statements worded differently across multiple posts—could clutter the database and obscure meaningful insights. Thus, it’s necessary to find an efficient and accurate method to detect and filter out such duplicates.

Understanding the Problem

Initially, my intuitive approach was to compare text strings directly. However, this method quickly proved inadequate because it fails when points convey the same idea but use different wording. For example:

• “EPS grew by 5% year-over-year”
• “Earnings increased slightly”

Both sentences essentially indicate earnings growth, yet a direct string comparison wouldn’t recognize them as duplicates. Therefore, a method capable of understanding semantic meaning is needed.