Narrative extraction, understanding and visualization is currently a popular topic and an important tool for humans interested in achieving a deeper understanding of text. Information Retrieval (IR), Natural Language Processing (NLP) and Machine Learning (ML) already offer many instruments that aid the exploration of narrative elements in text and within unstructured data. Despite evident advances in the last couple of years the problem of automatically representing narratives in a structured form, beyond the conventional identification of common events, entities and their relationships, is yet to be solved. This workshop held virtually onApril 1 st , 2021 co-located with the 43 rd European Conference on Information Retrieval (ECIR'21) aims at presenting and discussing current and future directions for IR, NLP, ML and other computational fields capable of improving the automatic understanding of narratives. It includes a session devoted to regular, short and demo papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of the area.
MotivationNarratives have long been studied in the computational field as a sequence or chain of events (happening) communicated by word (oral and written) and/or visually (through images, videos or other forms of representations). Over the years several methods borrowed from different computational areas, including Information Retrieval (IR), Natural Language Processing (NLP) and Machine Learning (ML) have been applied as a means to better understand the constituents of a narrative, their actors, events, entities and their relationship on time and space. Industries such as finance [1], business [5], news outlets [10], and health care [12] have been the main beneficiaries of the investment in this kind of technology. The ultimate goal is to offer users the chance to more quickly