Welcome to the 1st Joint Workshop on financial Narrative Processing and MultiLing financial Summarisation (FNP-FNS 2020) held at COLING 2020 in Barcelona, Spain. For future readers, it is worth noting the that workshop as well as the main conference were held as virtual events due to travel restrictions caused by the COVID-19 pandemic. Following the success of the First FNP 2018 at LREC’18 in Japan, the Second FNP 2019 at NoDaLiDa 2019 in Finland and as well as the Multiling 2019 financial narrative Summarisation task at RANLP in Bulgaria, we have received a great deal of positive feedback and interest in continuing the development of the financial narrative processing field, especially from our shared task participants. This has resulted in a collaborative workshop between the FNP and MultiLing workshop series to co-organise the 1st Joint Workshop on financial Narrative Processing and MultiLing financial Summarisation (FNP-FNS 2020). The 1st FNP-FNS workshop achieved our aim of supporting the rapidly growing area of financial text mining. We ran three different shared tasks focusing on text summarisation, structure detection and causal sentence detection, namely FNS, FinToc and FinCausal shared tasks respectively. The shared
tasks attracted more than 100 teams from different universities and organisations around the globe. The shared tasks resulted in the first large scale experimental results and state of the art methods applied mainly to financial data. This shows the importance and growth of this field and we want to continue to be associated with top NLP venues. The joint workshop focused mainly on the use of Natural Language Processing (NLP), Machine Learning (ML), and Corpus Linguistics (CL) methods related to all aspects of financial text summarisation, text mining and financial narrative processing (FNP). There is a growing interest in the application of automatic and computer-aided approaches for extracting, summarising, and analysing both qualitative and quantitative financial data. In recent years, previous manual small-scale research in the Accounting and Finance literature has been scaled up with the aid of NLP and ML methods, for example to examine approaches to retrieving structured content from financial reports, and to study the causes and consequences of corporate disclosure and financial reporting outcomes. The workshop organisers collaborated with two Artificial Intelligence (AI) firms: Fortia financial Solutions (www.fortia.fr) and Yseop (www.yseop.com). Both firms are pioneers in Artificial Intelligence, NLP and Natural Language Generation (NLG). Both firms work on applying those methods to automatically analyse and extract from financial documents and disclosures.