A major challenge in combating global human trafficking is the availability of actionable intelligence about trafficking events and operations. The lack of timely and structured data remains a significant bottleneck in the monitoring and mitigation of human trafficking. In this collaborative work with Love Justice International1, we aim to address this issue by developing an automated pipeline based on recent advances in natural language processing and machine learning to streamline the curation, analysis, and extraction of actionable intelligence from multi-sourced news media as Open-ources of Intelligence (OSINT). In our solution, we utilize and enhance the BERT Question Answering language model for information extraction from the unstructured text of the news. Furthermore, we develop algorithms for measuring the relevance and novelty of curated news articles to reduce the computation cost and redundant processing. Moreover, we evaluate the proposed pipeline on a dataset of annotated news articles containing actionable intelligence about victims and perpetrators of human trafficking.