In construction, there has always been a painful disconnect in reconciling parameters set in the master plan to what is actually happening in real-time on a project against the superintendent’s look-ahead. In the absence of an integrated solution, superintendents spend hours manually attempting to connect the master schedule with the look-ahead tasks written on trailer walls and sticky notes using papers or spreadsheets. This leads to frequent human error, poor communication between field and office and missing jobsite information created by the realities of weekly re-planning on-site. To address these inefficiencies, this paper explores the use of natural language processing (NLP) on both long-term (master) schedule and short-term (lookahead) plans to automatically learn and map their activities and tasks against one another. Using preliminary results from several commercial building projects, the potential of using an NLP seq2seq model as an Extractive and Abstractive Text Summarization technique is discussed in detail.