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| "text": "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.", |
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| "text": "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).", |
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| "text": "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.", |
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| "text": "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.", |
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| "text": "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.", |
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| "text": "We accepted 36 submissions to be presented in online oral and poster presentations. Each paper was reviewed by up to three reviewers. The submissions distribution is as follows: 6 main workshop papers and 30 shared task papers as follows: 10 papers accepted by the FNS shared task, 14 by the FinCausal shared task and 6 by the FinTOC shared task. Papers accepted in the main workshop were presented orally. All shared task papers were presented in 3 different poster sessions, one for each shared task. The papers covered a diverse set of topics in financial narratives processing reporting work on financial reports from different stock markets around the globe presenting analysis of financial reports and using state of the art NLP methods such as the use of latest word embeddings. The quantity and quality of the contributions to the workshop are strong indicators that there is a continuing and growing interest in the field of financial Natural Language Processing.", |
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| "text": "I would like to acknowledge all the hard work by the programme committee to make FNP-FNS a great success regardless of the challenging situations. I would also like to thank the submitting authors and the reviewers for the valuable feedback they provided. I hope these proceedings will serve as a valuable reference for researchers and practitioners in the field of financial narrative processing and NLP in general.", |
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| "text": "Dr Mahmoud El-Haj, General Chair, on behalf of the organizers of the FNP-FNS workshop, December 2020. ", |
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| "raw_text": "Saturday December 12, 2020 -Time zone \u2022 Greenwich Mean Time (GMT) (continued) 13:20-14:50 Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, Eduardo Brito, Clayton Chap- man, Paul Mayer and Rafet Sifa 13:20-14:50 NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder Pei-Wei Kao, Chung-Chi Chen, Hen-Hsen Huang and Hsin-Hsi Chen 13:20-14:50 FiNLP at FinCausal 2020 Task 1: Mixture of BERTs for Causal Sentence Identifi- cation in Financial Texts Sarthak Gupta 13:20-14:50 ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers Zsolt Sz\u00e1nt\u00f3 and G\u00e1bor Berend 13:20-14:50 ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Ap- proach for Causality Identification in Financial Documents G\u00f6kberk \u00d6zenir and\u0130lknur Karadeniz 13:20-14:50 Domino at FinCausal 2020, Task 1 and 2: Causal Extraction System Sharanya Chakravarthy, Tushar Kanakagiri, Karthik Radhakrishnan and Anjana Umapathy 13:20-14:50 IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Em- beddings in Financial Reports Arka Mitra, Harshvardhan Srivastava and Yugam Tiwari 14:50-15:50 Session 3: Financial Narrative Summarisation (FNS) 14:50-15:50 Extractive Financial Narrative Summarisation based on DPPs lei Li, yafei Jiang and yinan Liu 14:50-15:50 PoinT-5: Pointer Network and T-5 based Financial Narrative Summarisation Abhishek Singh 14:50-15:50 Combining financial word embeddings and knowledge-based features for financial text summarization UC3M-MC System at FNS-2020", |
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| "raw_text": "Saturday December 12, 2020 -Time zone \u2022 Greenwich Mean Time (GMT) (continued) 14:50-15:50 SCE-SUMMARY at the FNS 2020 shared task Marina Litvak, Natalia Vanetik and Zvi Puchinsky 14:50-15:50 Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples Amit Vhatkar, Pushpak Bhattacharyya and Kavi Arya 14:50-15:50 AMEX AI-Labs: An Investigative Study on Extractive Summarization of Financial Documents Piyush Arora and Priya Radhakrishnan 14:50-15:50 Extractive Summarization System for Annual Reports Abderrahim Ait Azzi and Juyeon Kang 14:50-15:50 SUMSUM@FNS-2020 Shared Task Siyan Zheng, Anneliese Lu and Claire Cardie 15:50-16:20 Session 4: Financial Document Structure Extraction (FinTOC) 15:50-16:20 AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation Dhruv Premi, Amogh Badugu and Himanshu Sharad Bhatt 15:50-16:20 UWB@FinTOC-2020 Shared Task: Financial Document Title Detection Tom\u00e1\u0161 Hercig and Pavel Kral 15:50-16:20 Taxy.io@FinTOC-2020: Multilingual Document Structure Extraction using Trans- fer Learning Frederic Haase and Steffen Kirchhoff 15:50-16:20 DNLP@FinTOC'20: Table of Contents Detection in Financial Documents Dijana Kosmajac, Stacey Taylor and Mozhgan Saeidi 15:50-16:20 Daniel@FinTOC'2 Shared Task: Title Detection and Structure Extraction Emmanuel Giguet, Ga\u00ebl Lejeune and Jean-Baptiste Tanguy 16:20-16:30 Break Saturday December 12, 2020 -Time zone \u2022 Greenwich Mean Time (GMT) (continued) 16:30-17:10 Session 5: Main Workshop Papers 16:30-16:35 A Computational Analysis of Financial and Environmental Narratives within Finan- cial Reports and its Value for Investors Felix Armbrust, Henry Sch\u00e4fer and Roman Klinger 16:37-16:42 Information Extraction from Federal Open Market Committee Statements Oana Frunza 16:44-16:49 Mitigating Silence in Compliance Terminology during Parsing of Utterances Esme Manandise and Conrad de Peuter 16:51-16:56 Hierarchical summarization of financial reports with RUNNER Marina Litvak, Natalia Vanetik and Zvi Puchinsky 16:58-17:03 Predicting Modality in Financial Dialogue Kilian Theil and Heiner Stuckenschmidt 17:05-17:10 Extracting Fine-Grained Economic Events from Business News Gilles Jacobs and Veronique Hoste 17:10-17:15 Short Break Saturday December 12, 2020 -Time zone \u2022 Greenwich Mean Time (GMT) (continued) 17:15-18:00 Session 6: Open Discussion 17:15-18:00 Open Discussion Mahmoud El-Haj and Paul Rayson xv", |
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| "FIGREF0": { |
| "num": null, |
| "text": "Financial Narrative Summarisation Shared Task (FNS 2020) Mahmoud El-Haj, Ahmed AbuRa'ed, Marina Litvak, Nikiforos Pittaras and George The Financial Document Structure Extraction Shared task (FinToc 2020)Najah-Imane Bentabet,R\u00e9mi JUGE, Ismail El Maarouf, Virginie Mouilleron, The Financial Document Causality Detection SharedTask(FinCausal 2020) Dominique Mariko, Hanna Abi-Akl, Estelle Labidurie, Stephane Durfort, Hugues De Mazancourt and Mahmoud El-Haj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection Raksha Agarwal, Ishaan Verma and Niladri Chatterjee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents Guillaume Becquin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 LIORI at the FinCausal 2020 Shared task Denis Gordeev, Adis Davletov, Alexey Rey and Nikolay Arefiev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection Toshiya Imoto and Tomoki Ito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 UPB at FinCausal-2020, Tasks 1 & 2: Causality Analysis in Financial Documents using Pretrained Language Models Marius Ionescu, Andrei-Marius Avram, George-Andrei Dima, Dumitru-Clementin Cercel and Mihai Dascalu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models. Hariharan R L and Anand Kumar M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, Eduardo Brito, Clayton Chapman, Paul Mayer and Rafet Sifa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder Pei-Wei Kao, Chung-Chi Chen, Hen-Hsen Huang and Hsin-Hsi Chen . . . . . . . . . . . . . . . . . . . . . . . . 69 FiNLP at FinCausal 2020 Task 1: Mixture of BERTs for Causal Sentence Identification in Financial Texts Sarthak Gupta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers Zsolt Sz\u00e1nt\u00f3 and G\u00e1bor Berend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents G\u00f6kberk \u00d6zenir and\u0130lknur Karadeniz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85", |
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