pwc-archive/papers-with-abstracts huggingface dataset:
LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection
Authors: Raksha Agarwal, Ishaan Verma, Niladri Chatterjee
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Natural Language Understanding

Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.
GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents
Authors: Guillaume Becquin
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Task 2

This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used to detect the number of cause and effect clauses in a document and extract them. The proposed system achieved a weighted-average F1 score of more than 95% for the official blind test set during the post-evaluation phase and exact clauses match for 83% of the documents.
LIORI at the FinCausal 2020 Shared task
Authors: Denis Gordeev, Adis Davletov, Alexey Rey, Nikolay Arefiev
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Sentence

In this paper, we describe the results of team LIORI at the FinCausal 2020 Shared task held as a part of the 1st Joint Workshop on Financial Narrative Processing and MultiLingual Financial Summarisation. The shared task consisted of two subtasks: classifying whether a sentence contains any causality and labelling phrases that indicate causes and consequences. Our team ranked 1st in the first subtask and 4th in the second one. We used Transformer-based models with joint-task learning and their ensembles.
JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection
Authors: Toshiya Imoto, Tomoki Ito
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Task 2

This paper describes the approach we built for the Financial Document Causality Detection Shared Task (FinCausal-2020) Task 2: Cause and Effect Detection. Our approach is based on a multi-class classifier using BiLSTM with Graph Convolutional Neural Network (GCN) trained by minimizing the binary cross entropy loss. In our approach, we have not used any extra data source apart from combining the trial and practice dataset. We achieve weighted F1 score to 75.61 percent and are ranked at 7-th place.
UPB at FinCausal-2020, Tasks 1 & 2: Causality Analysis in Financial Documents using Pretrained Language Models
Authors: Marius Ionescu, Andrei-Marius Avram, George-Andrei Dima, Dumitru-Clementin Cercel, Mihai Dascalu
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Binary Classification

Financial causality detection is centered on identifying connections between different assets from financial news in order to improve trading strategies. FinCausal 2020 - Causality Identification in Financial Documents – is a competition targeting to boost results in financial causality by obtaining an explanation of how different individual events or chain of events interact and generate subsequent events in a financial environment. The competition is divided into two tasks: (a) a binary classification task for determining whether sentences are causal or not, and (b) a sequence labeling task aimed at identifying elements related to cause and effect. Various Transformer-based language models were fine-tuned for the first task and we obtained the second place in the competition with an F1-score of 97.55% using an ensemble of five such language models. Subsequently, a BERT model was fine-tuned for the second task and a Conditional Random Field model was used on top of the generated language features; the system managed to identify the cause and effect relationships with an F1-score of 73.10%. We open-sourced the code and made it available at: https://github.com/avramandrei/FinCausal2020.
NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models.
Authors: Hariharan R L, Anand Kumar M
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Financial Analysis

FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don’t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.
Fraunhofer IAIS at FinCausal 2020, Tasks 1 & 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents
Authors: Maren Pielka, Rajkumar Ramamurthy, Anna Ladi, Eduardo Brito, Clayton Chapman, Paul Mayer, Rafet Sifa
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Task 2

The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods for identifying causality, and extracting causal subsequences. Our models yield promising results on both sub-tasks, with the prospect of further improvement given more time and computing resources. With respect to task 1, we achieved an F1 score of 0.9429 on the evaluation data, and a corresponding ranking of 12/14. For task 2, we were ranked 6/10, with an F1 score of 0.76 and an ExactMatch score of 0.1912.
NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder
Authors: Pei-Wei Kao, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Decoder, Task 2

In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in financial news and propose an approach, which combines the BIO scheme with the Viterbi decoder for addressing this challenge. Our approach is ranked the first in the official run of cause-effect detection (Task 2) of the FinCausal-2020 shared task. We not only report the implementation details and ablation analysis in this paper, but also publish our code for academic usage.
FiNLP at FinCausal 2020 Task 1: Mixture of BERTs for Causal Sentence Identification in Financial Texts
Authors: Sarthak Gupta
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Data Augmentation, Position, Sentence

This paper describes our system developed for the sub-task 1 of the FinCausal shared task in the FNP-FNS workshop held in conjunction with COLING-2020. The system classifies whether a financial news text segment contains causality or not. To address this task, we fine-tune and ensemble the generic and domain-specific BERT language models pre-trained on financial text corpora. The task data is highly imbalanced with the majority non-causal class; therefore, we train the models using strategies such as under-sampling, cost-sensitive learning, and data augmentation. Our best system achieves a weighted F1-score of 96.98 securing 4th position on the evaluation leaderboard. The code is available at https://github.com/sarthakTUM/fincausal
ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers
Authors: Zsolt Szántó, Gábor Berend
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Language Modeling, Language Modelling

This paper introduces our efforts at the FinCasual shared task for modeling causality in financial utterances. Our approach uses the commonly and successfully applied strategy of fine-tuning a transformer-based language model with a twist, i.e. we modified the training and inference mechanism such that our model produces multiple predictions for the same instance. By designing such a model that returns k>1 predictions at the same time, we not only obtain a more resource efficient training (as opposed to fine-tuning some pre-trained language model k independent times), but our results indicate that we are also capable of obtaining comparable or even better evaluation scores that way. We compare multiple strategies for combining the k predictions of our model. Our submissions got ranked third on both subtasks of the shared task.
ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents
Authors: Gökberk Özenir, İlknur Karadeniz
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Binary Classification, Task 2

This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94%) , which requires less time compared to the BERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52%), we have obtained better results (F-score 60%). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).
Domino at FinCausal 2020, Task 1 and 2: Causal Extraction System
Authors: Sharanya Chakravarthy, Tushar Kanakagiri, Karthik Radhakrishnan, Anjana Umapathy
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Information Retrieval, Question Answering, Retrieval, token-classification, Token Classification

Automatic identification of cause-effect relationships from data is a challenging but important problem in artificial intelligence. Identifying semantic relationships has become increasingly important for multiple downstream applications like Question Answering, Information Retrieval and Event Prediction. In this work, we tackle the problem of causal relationship extraction from financial news using the FinCausal 2020 dataset. We tackle two tasks - 1) Detecting the presence of causal relationships and 2) Extracting segments corresponding to cause and effect from news snippets. We propose Transformer based sequence and token classification models with post-processing rules which achieve an F1 score of 96.12 and 79.60 on Tasks 1 and 2 respectively.
IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports
Authors: Arka Mitra, Harshvardhan Srivastava, Yugam Tiwari
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: regression, Sentence, Sentence Embeddings

The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.
Extractive Financial Narrative Summarisation based on DPPs
Authors: Lei LI, Yafei Jiang, Yinan Liu
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Diversity, Point Processes

We participate in the FNS-Summarisation 2020 shared task to be held at FNP 2020 workshop at COLING 2020. Based on Determinantal Point Processes (DPPs), we build an extractive automatic financial summarisation system for the specific task. In this system, we first analyze the long report data to select the important narrative parts and generate an intermediate document. Next, we build the kernel Matrix L for the intermediate document, which represents the quality of its sentences. On the basis of L, we then can use the DPPs sampling algorithm to choose those sentences with high quality and diversity as the final summary sentences.
PoinT-5: Pointer Network and T-5 based Financial Narrative Summarisation
Authors: Abhishek Singh
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Sentence

Companies provide annual reports to their shareholders at the end of the financial year that de-scribes their operations and financial conditions. The average length of these reports is 80, andit may extend up to 250 pages long. In this paper, we propose our methodology PoinT-5 (thecombination of Pointer Network and T-5 (Test-to-text transfer Transformer) algorithms) that weused in the Financial Narrative Summarisation (FNS) 2020 task. The proposed method usesPointer networks to extract important narrative sentences from the report, and then T-5 is used toparaphrase extracted sentences into a concise yet informative sentence. We evaluate our methodusing Rouge-N (1,2), L, and SU4. The proposed method achieves the highest precision scores inall the metrics and highest F1 scores in three out of four evaluation metrics that are Rouge 1, 2,and LCS and only solution to cross MUSE solution baseline in Rouge-LCS metrics.
Combining financial word embeddings and knowledge-based features for financial text summarization UC3M-MC System at FNS-2020
Authors: Jaime Baldeon Suarez, Paloma Martínez, Jose Luis Martínez
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Text Summarization, Word Embeddings

This paper describes the systems proposed by HULAT research group from Universidad Carlos III de Madrid (UC3M) and MeaningCloud (MC) company to solve the FNS 2020 Shared Task on summarizing financial reports. We present a narrative extractive approach that implements a statistical model comprised of different features that measure the relevance of the sentences using a combination of statistical and machine learning methods. The key to the model’s performance is its accurate representation of the text, since the word embeddings used by the model have been trained with the summaries of the training dataset and therefore capture the most salient information from the reports. The systems’ code can be found at https://github.com/jaimebaldeon/FNS-2020.
End-to-end Training For Financial Report Summarization
Authors: Moreno La Quatra, Luca Cagliero
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

Quoted companies are requested to periodically publish financial reports in textual form. The annual financial reports typically include detailed financial and business information, thus giving relevant insights into company outlooks. However, a manual exploration of these financial reports could be very time consuming since most of the available information can be deemed as non-informative or redundant by expert readers. Hence, an increasing research interest has been devoted to automatically extracting domain-specific summaries, which include only the most relevant information. This paper describes the SumTO system architecture, which addresses the Shared Task of the Financial Narrative Summarisation (FNS) 2020 contest. The main task objective is to automatically extract the most informative, domain-specific textual content from financial, English-written documents. The aim is to create a summary of each company report covering all the business-relevant key points. To address the above-mentioned goal, we propose an end-to-end training method relying on Deep NLP techniques. The idea behind the system is to exploit the syntactic overlap between input sentences and ground-truth summaries to fine-tune pre-trained BERT embedding models, thus making such models tailored to the specific context. The achieved results confirm the effectiveness of the proposed method, especially when the goal is to select relatively long text snippets.
SCE-SUMMARY at the FNS 2020 shared task
Authors: Marina Litvak, Natalia Vanetik, Zvi Puchinsky
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.
Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples
Authors: Amit Vhatkar, Pushpak Bhattacharyya, Kavi Arya
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Extractive Text Summarization, Sentence, Text Summarization

In our research work, we represent the content of the sentence in graphical form after extracting triples from the sentences. In this paper, we will discuss novel methods to generate an extractive summary by scoring the triples. Our work has also touched upon sequence-to-sequence encoding of the content of the sentence, to classify it as a summary or a non-summary sentence. Our findings help to decide the nature of the sentences forming the summary and the length of the system generated summary as compared to the length of the reference summary.
AMEX AI-Labs: An Investigative Study on Extractive Summarization of Financial Documents
Authors: Piyush Arora, Priya Radhakrishnan
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Document Summarization, Extractive Summarization

We describe the work carried out by AMEX AI-LABS on an extractive summarization benchmark task focused on Financial Narratives Summarization (FNS). This task focuses on summarizing annual financial reports which poses two main challenges as compared to typical news document summarization tasks : i) annual reports are more lengthier (average length about 80 pages) as compared to typical news documents, and ii) annual reports are more loosely structured e.g. comprising of tables, charts, textual data and images, which makes it challenging to effectively summarize. To address this summarization task we investigate a range of unsupervised, supervised and ensemble based techniques. We find that ensemble based techniques perform relatively better as compared to using only the unsupervised and supervised based techniques. Our ensemble based model achieved the highest rank of 9 out of 31 systems submitted for the benchmark task based on Rouge-L evaluation metric.
Extractive Summarization System for Annual Reports
Authors: Abderrahim Ait Azzi, Juyeon Kang
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Extractive Summarization

In this paper, we report on our experiments in building a summarization system for generating summaries from annual reports. We adopt an “extractive” summarization approach in our hybrid system combining neural networks and rules-based algorithms with the expectation that such a system may capture key sentences or paragraphs from the data. A rules-based TOC (Table Of Contents) extraction and a binary classifier of narrative section titles are main components of our system allowing to identify narrative sections and best candidates for extracting final summaries. As result, we propose one to three summaries per document according to the classification score of narrative section titles.
SUMSUM@FNS-2020 Shared Task
Authors: Siyan Zheng, Anneliese Lu, Claire Cardie
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Extractive Summarization

This paper describes the SUMSUM systems submitted to the Financial Narrative Summarization Shared Task (FNS-2020). We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sections based on the ToC, and applies a BERT-based classifier to each section to determine whether it should be included in the summary. Our best system ranks 4<sup>th</sup>, 1<sup>st</sup>, 2<sup>nd</sup> and 17<sup>th</sup> on the Rouge-1, Rouge-2, Rouge-SU4, and Rouge-L official metrics, respectively. We also report results on the validation set using an alternative set of Rouge-based metrics that measure performance with respect to the best-matching of the available gold summaries.
AMEX-AI-LABS: Investigating Transfer Learning for Title Detection in Table of Contents Generation
Authors: Dhruv Premi, Amogh Badugu, Himanshu Sharad Bhatt
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Transfer Learning

We present a transfer learning approach for Title Detection in FinToC 2020 challenge. Our proposed approach relies on the premise that the geometric layout and character features of the titles and non-titles can be learnt separately from a large corpus, and their learning can then be transferred to a domain-specific dataset. On a domain-specific dataset, we train a Deep Neural Net on the text of the document along with a pre-trained model for geometric and character features. We achieved an F-Score of 83.25 on the test set and secured top rank in the title detection task in FinToC 2020.
UWB@FinTOC-2020 Shared Task: Financial Document Title Detection
Authors: Tomáš Hercig, Pavel Kral
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

This paper describes our system created for the Financial Document Structure Extraction Shared Task (FinTOC-2020): Title Detection. We rely on the Apache PDFBox library to extract text and all additional information e.g. font type and font size from the financial prospectuses. Our constrained system uses only the provided training data without any additional external resources. Our system is based on the Maximum Entropy classifier and various features including font type and font size. Our system achieves F1 score 81% and #1 place in the French track and F1 score 77% and #2 place among 5 participating teams in the English track.