This study introduces a new Natural Language Generation (NLG) task – Unit Claim Identification. The task aims to extract every piece of verifiable information from a headline. The Unit Claim identification has applications in other domains; such as fact-checking where the identification of each verifiable information from a check-worthy statement can lead to an effective fact-check. Moreover, the extracting of the unit claims from headlines can identify a misleading news article, by mapping evidence from contents. For addressing the unit claim identification problem, we outlined a set of guidelines for data annotation, arranged in-house training for the annotators and obtained a small dataset. We explored two potential approaches - 1) Rule-based approach and 2) Deep learning-based approach and compared their performances. Although the performance of the deep learning-based approach was not very effective due to small number of training instances, the rule-based approach shoa promising result in terms of precision (65.85%)
