pwc-archive/papers-with-abstracts huggingface dataset:
Extractive and Abstractive Summarization Methods for Financial Narrative Summarization in English, Spanish and Greek
Authors: Alejandro Vaca, Alba Segurado, David Betancur, Álvaro Barbero Jiménez
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Abstractive Text Summarization, Decoder, Extractive Summarization, Sentence, Translation

This paper describes the three summarization systems submitted to the Financial Narrative Summarization Shared Task (FNS-2022). We developed a task-specific extractive summarization method for the reports in English. It was based on a sequence classification task whose objective was to find the sentence where the summary begins. On the other hand, since the summaries for the reports in Spanish and Greek were not extractive, we used an abstractive strategy for each of the languages. In particular, we created a new Encoder-Decoder architecture in Spanish, MariMari, based on an existing Encoding-only model; we also trained multilingual Encoder-Decoder models for this task. Finally, the summaries for the reports in Greek were obtained with a translation-summary-translation system in which the reports were translated to English and summarised, and then the summaries were translated back to Greek.
DiMSum: Distributed and Multilingual Summarization of Financial Narratives
Authors: Neelesh Shukla, Amit Vaid, Raghu Katikeri, Sangeeth Keeriyadath, Msp Raja
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Document AI, Document Summarization, Extractive Document Summarization

This paper was submitted for Financial Narrative Summarization (FNS) task in FNP-2022 workshop. The objective of the task was to generate not more than 1000 words summaries for the annual financial reports written in English, Spanish and Greek languages. The central idea of this paper is to demonstrate automatic ways of identifying key narrative sections and their contributions towards generating summaries of financial reports. We have observed a few limitations in the previous works: First, the complete report was being considered for summary generation instead of key narrative sections. Second, many of the works followed manual or heuristic-based techniques to identify narrative sections. Third, sentences from key narrative sections were abruptly dropped to limit the summary to the desired length. To overcome these shortcomings, we introduced a novel approach to automatically learn key narrative sections and their weighted contributions to the reports. Since the summaries may come from various parts of the reports, the summary generation process was distributed amongst the key narrative sections based on the weights identified, later combined to have an overall summary. We also showcased that our approach is adaptive to various report formats and languages.
Transformer-based Models for Long Document Summarisation in Financial Domain
Authors: Urvashi Khanna, Samira Ghodratnama, Diego Moll ́a, Amin Beheshti
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Decoder

Summarisation of long financial documents is a challenging task due to the lack of large-scale datasets and the need for domain knowledge experts to create human-written summaries. Traditional summarisation approaches that generate a summary based on the content cannot produce summaries comparable to human-written ones and thus are rarely used in practice. In this work, we use the Longformer-Encoder-Decoder (LED) model to handle long financial reports. We describe our experiments and participating systems in the financial narrative summarisation shared task. Multi-stage fine-tuning helps the model generalise better on niche domains and avoids the problem of catastrophic forgetting. We further investigate the effect of the staged fine-tuning approach on the FNS dataset. Our systems achieved promising results in terms of ROUGE scores on the validation dataset.
Financial Narrative Summarisation Using a Hybrid TF-IDF and Clustering Summariser: AO-Lancs System at FNS 2022
Authors: Mahmoud El-Haj, Andrew Ogden
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Clustering, Sentence

This paper describes the HTAC system submitted to the Financial Narrative Summarization Shared Task (FNS-2022). A methodology implementing Financial narrative Processing (FNP) to summarise financial annual reports, named Hybrid TF-IDF and Clustering (HTAC). This involves a hybrid approach combining TF-IDF sentence ranking as an NLP tool with a state-of-the-art Clustering Machine learning model to produce short 1000-word summaries of long financial annual reports. These Annual Reports are a legal responsibility of public companies and are in excess of 50,000 words. The model extracts the crucial information from these documents, discarding the extraneous content, leaving only the crucial information in a shorter, non-redundant summary. Producing summaries that are more effective than summaries produced by two pre-existing generic summarisers.
The Financial Document Structure Extraction Shared Task (FinTOC 2022)
Authors: Juyeon Kang, Abderrahim Ait Azzi, Sandra Bellato, Blanca Carbajo Coronado, Mahmoud El-Haj, Ismail El Maarouf, Mei Gan, Ana Gisbert, Antonio Moreno Sandoval
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6

This paper describes the FinTOC-2022 Shared Task on the structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 4th Financial Narrative Processing Workshop (FNP 2022), held jointly at The 13th Edition of the Language Resources and Evaluation Conference (LREC 2022), Marseille, France (El-Haj et al., 2022). This shared task aimed to stimulate research in systems for extracting table-of-contents (TOC) from investment documents (such as financial prospectuses) by detecting the document titles and organizing them hierarchically into a TOC. For the forth edition of this shared task, three subtasks were presented to the participants: one with English documents, one with French documents and the other one with Spanish documents. This year, we proposed a different and revised dataset for English and French compared to the previous editions of FinTOC and a new dataset for Spanish documents was added. The task attracted 6 submissions for each language from 4 teams, and the most successful methods make use of textual, structural and visual features extracted from the documents and propose classification models for detecting titles and TOCs for all of the subtasks.
ISPRAS@FinTOC-2022 Shared Task: Two-stage TOC Generation Model
Authors: Anastasiia Bogatenkova, Oksana Vladimirovna Belyaeva, Andrew Igorevich Perminov, Ilya Sergeevich Kozlov
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Vocal Bursts Valence Prediction

This work is connected with participation in FinTOC-2022 Shared Task: “Financial Document Structure Extraction”. The competition contains two subtasks: title detection and TOC generation. We describe an approach for solving these tasks and propose the pipeline, consisting of extraction of document lines and existing TOC, feature matrix forming and classification. Classification model consists of two classifiers: the first binary classifier separates title lines from non-title, the second one determines the title level. In the title detection task, we got 0.900, 0.778 and 0.558 F1 measure, in the TOC generation task we got 63.1, 41.5 and 40.79 the harmonic mean of Inex F1 score and Inex level accuracy for English, French and Spanish documents respectively. With these results, our approach took first place among English and French submissions and second place among Spanish submissions. As a team, we took first place in the competition in English and French categories and second place in the competition in Spanish.
swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain
Authors: Pierluigi Cassotti, Cataldo Musto, Marco DeGemmis, Georgios Lekkas, Giovanni Semeraro
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6

In this paper, we introduce the results of our submitted system to the FinTOC 2022 task. We address the task using a two-stage process: first, we detect titles using Document Image Analysis, then we train a supervised model for the hierarchical level prediction. We perform Document Image Analysis using a pre-trained Faster R-CNN on the PublyaNet dataset. We fine-tuned the model on the FinTOC 2022 training set. We extract orthographic and layout features from detected titles and use them to train a Random Forest model to predict the title level. The proposed system ranked #1 on both Title Detection and the Table of Content extraction tasks for Spanish. The system ranked #3 on both the two subtasks for English and French.
GREYC@FinTOC-2022: Handling Document Layout and Structure in Native PDF Bundle of Documents
Authors: Emmanuel Giguet, Nadine Lucas
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Boundary Detection

n this paper, we present our contribution to the FinTOC-2022 Shared Task “Financial Document Structure Extraction”. We participated in the three tracks dedicated to English, French and Spanish document processing. Our main contribution consists in considering financial prospectus as a bundle of documents, i.e., a set of merged documents, each with their own layout and structure. Therefore, Document Layout and Structure Analysis (DLSA) first starts with the boundary detection of each document using general layout features. Then, the process applies inside each single document, taking advantage of the local properties. DLSA is achieved considering simultaneously text content, vectorial shapes and images embedded in the native PDF document. For the Title Detection task in English and French, we observed a significant improvement of the F-measures for Title Detection compared with those obtained during our previous participation.
The Financial Causality Extraction Shared Task (FinCausal 2022)
Authors: Dominique Mariko, Hanna Abi-Akl, Kim Trottier, Mahmoud El-Haj
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. The task focuses on detecting if an object, an event or a chain of events is considered a cause for a prior event. This shared task focuses on determining causality associated with a quantified fact. An event is defined as the arising or emergence of a new object or context in regard to a previous situation. Therefore, the task will emphasise the detection of causality associated with transformation of financial objects embedded in quantified facts. A total number of 7 teams submitted system runs to the FinCausal task and contributed with a system description paper. FinCausal shared task is associated with the 4th Financial Narrative Processing Workshop (FNP 2022) (El-Haj et al., 2022) which is held at the The 13th Language Resources and Evaluation Conference (LREC 2022) in Marseille, France, on June 24, 2022.
SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models
Authors: Anik Saha, Jian Ni, Oktie Hassanzadeh, Alex Gittens, Kavitha Srinivas, Bulent Yener
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Language Modeling, Language Modelling

Causal information extraction is an important task in natural language processing, particularly in finance domain. In this work, we develop several information extraction models using pre-trained transformer-based language models for identifying cause and effect text spans from financial documents. We use FinCausal 2021 and 2022 data sets to train span-based and sequence tagging models. Our ensemble of sequence tagging models based on the RoBERTa-Large pre-trained language model achieves an F1 score of 94.70 with Exact Match score of 85.85 and obtains the 1st place in the FinCausal 2022 competition.
Multilingual Financial Documentation Summarization by Team_Tredence for FNS2022
Authors: Manish Pant, Ankush Chopra
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Abstractive Text Summarization, Document Summarization, Semantic Similarity, Semantic Textual Similarity

This paper describes multi-lingual long document summarization systems submitted to the Financial Narrative Summarization Shared Task (FNS 2022 ) by Team-Tredence. We developed task-specific summarization methods for 3 languages – English, Spanish and Greek. The solution is divided into two parts, where a RoBERTa model was finetuned to identify/extract summarizing segments from English documents and T5 based models were used for summarizing Spanish and Greek documents. A purely extractive approach was applied to summarize English documents using data-specific heuristics. An mT5 model was fine-tuned to identify potential narrative sections for Greek and Spanish, followed by finetuning mT5 and T5(Spanish version) for abstractive summarization task. This system also features a novel approach for generating summarization training dataset using long document segmentation and the semantic similarity across segments. We also introduce an N-gram variability score to select sub-segments for generating more diverse and informative summaries from long documents.
DCU-Lorcan at FinCausal 2022: Span-based Causality Extraction from Financial Documents using Pre-trained Language Models
Authors: Chenyang Lyu, Tianbo Ji, Quanwei Sun, Liting Zhou
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6

In this paper, we describe our DCU-Lorcan system for the FinCausal 2022 shared task: span-based cause and effect extraction from financial documents. We frame the FinCausal 2022 causality extraction task as a span extraction/sequence labeling task, our submitted systems are based on the contextualized word representations produced by pre-trained language models and linear layers predicting the label for each word, followed by post-processing heuristics. In experiments, we employ pre-trained language models including DistilBERT, BERT and SpanBERT. Our best performed system achieves F-1, Recall, Precision and Exact Match scores of 92.76, 92.77, 92.76 and 68.60 respectively. Additionally, we conduct experiments investigating the effect of data size to the performance of causality extraction model and an error analysis investigating the outputs in predictions.
LIPI at FinCausal 2022: Mining Causes and Effects from Financial Texts
Authors: Sohom Ghosh, Sudip Naskar
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6

While reading financial documents, investors need to know the causes and their effects. This empowers them to make data-driven decisions. Thus, there is a need to develop an automated system for extracting causes and their effects from financial texts using Natural Language Processing. In this paper, we present the approach our team LIPI followed while participating in the FinCausal 2022 shared task. This approach is based on the winning solution of the first edition of FinCausal held in the year 2020.
iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph
Authors: Ziwei Xu, Rungsiman Nararatwong, Natthawut Kertkeidkachorn, Ryutaro Ichise
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: graph construction, Graph Embedding

The application of span detection grows fast along with the increasing need of understanding the causes and effects of events, especially in the finance domain. However, once the syntactic clues are absent in the text, the model tends to reverse the cause and effect spans. To solve this problem, we introduce graph construction techniques to inject cause-effect graph knowledge for graph embedding. The graph features combining with BERT embedding, then are used to predict the cause effect spans. The results show our proposed graph builder method outperforms the other methods w/wo external knowledge.
ExpertNeurons at FinCausal 2022 Task 2: Causality Extraction for Financial Documents
Authors: Joydeb Mondal, Nagaraj Bhat, Pramir Sarkar, Shahid Reza
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Task 2

In this paper describes the approach which we have built for causality extraction from the financial documents that we have submitted for FinCausal 2022 task 2. We proving a solution with intelligent pre-processing and post-processing to detect the number of cause and effect in a financial document and extract them. Our given approach achieved 90% as F1 score(weighted-average) for the official blind evaluation dataset.
ATL at FinCausal 2022: Transformer Based Architecture for Automatic Causal Sentence Detection and Cause-Effect Extraction
Authors: Abir Naskar, Tirthankar Dasgupta, Sudeshna Jana, Lipika Dey
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: Sentence, Task 2

Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on restricted domain data sets. With the advent of big data, and the recent popularization of deep learning, the paradigm to tackle this problem has slowly shifted. In this work we proposed a transformer based architecture to automatically detect causal sentences from textual mentions and then identify the corresponding cause-effect relations. We describe our submission to the FinCausal 2022 shared task based on this method. Our model achieves a F1-score of 0.99 for the Task-1 and F1-score of 0.60 for Task-2 on the shared task data set on financial documents.
MNLP at FinCausal2022: Nested NER with a Generative Model
Authors: Jooyeon Lee, Luan Huy Pham, Özlem Uzuner
Date: 2022-06-01 00:00:00
Proceeding: FNP (LREC) 2022 6
Tasks: NER

This paper describes work performed for the FinCasual 2022 Shared Task “Financial Document Causality Detection” (FinCausal 2022). As the name implies, the task involves extraction of casual and consequential elements from financial text. Our approach focuses employing Nested NER using the Text-to-Text Transformer (T5) generative transformer models while applying different combinations of datasets and tagging methods. Our system reports accuracy of 79% in Exact Match comparison and F-measure score of 92% token level measurement.
Synthesis for the Kinematic Control of Identity in Sign Language
Authors: Félix Bigand, Elise Prigent, Annelies Braffort
Date: 2022-06-01 00:00:00
Proceeding: SLTAT (LREC) 2022 6

Sign Language (SL) animations generated from motion capture (mocap) of real signers convey critical information about their identity. It has been suggested that this information is mostly carried by statistics of the movements kinematics. Manipulating these statistics in the generation of SL movements could allow controlling the identity of the signer, notably to preserve anonymity. This paper tests this hypothesis by presenting a novel synthesis algorithm that manipulates the identity-specific statistics of mocap recordings. The algorithm produced convincing new versions of French Sign Language discourses, which accurately modulated the identity prediction of a machine learning model. These results open up promising perspectives toward the automatic control of identity in the motion animation of virtual signers.
Analysis of Torso Movement for Signing Avatar Using Deep Learning
Authors: Shatabdi Choudhury
Date: 2022-06-01 00:00:00
Proceeding: SLTAT (LREC) 2022 6
Tasks: Deep Learning, Translation

Avatars are virtual or on-screen representations of a human used in various roles for sign language display, including translation and educational tools. Though the ability of avatars to portray acceptable sign language with believable human-like motion has improved in recent years, many still lack the naturalness and supporting motions of human signing. Such details are generally not included in the linguistic annotation. Nevertheless, these motions are highly essential to displaying lifelike and communicative animations. This paper presents a deep learning model for use in a signing avatar. The study focuses on coordinating torso movements and other human body parts. The proposed model will automatically compute the torso rotation based on the avatar’s wrist positions. The resulting motion can improve the user experience and engagement with the avatar.
Isolated Sign Recognition using ASL Datasets with Consistent Text-based Gloss Labeling and Curriculum Learning
Authors: Konstantinos M. Dafnis, Evgenia Chroni, Carol Neidle, Dimitri Metaxas
Date: 2022-06-01 00:00:00
Proceeding: SLTAT (LREC) 2022 6

We present a new approach for isolated sign recognition, which combines a spatial-temporal Graph Convolution Network (GCN) architecture for modeling human skeleton keypoints with late fusion of both the forward and backward video streams, and we explore the use of curriculum learning. We employ a type of curriculum learning that dynamically estimates, during training, the order of difficulty of each input video for sign recognition; this involves learning a new family of data parameters that are dynamically updated during training. The research makes use of a large combined video dataset for American Sign Language (ASL), including data from both the American Sign Language Lexicon Video Dataset (ASLLVD) and the Word-Level American Sign Language (WLASL) dataset, with modified gloss labeling of the latter—to ensure 1-1 correspondence between gloss labels and distinct sign productions, as well as consistency in gloss labeling across the two datasets. This is the first time that these two datasets have been used in combination for isolated sign recognition research. We also compare the sign recognition performance on several different subsets of the combined dataset, varying in, e.g., the minimum number of samples per sign (and therefore also in the total number of sign classes and video examples).
Example-based Multilinear Sign Language Generation from a Hierarchical Representation
Authors: Boris Dauriac, Annelies Braffort, Elise Bertin-Lemée
Date: 2022-06-01 00:00:00
Proceeding: SLTAT (LREC) 2022 6
Tasks: Text Generation

This article presents an original method for automatic generation of sign language (SL) content by means of the animation of an avatar, with the aim of creating animations that respect as much as possible linguistic constraints while keeping bio-realistic properties. This method is based on the use of a domain-specific bilingual corpus richly annotated with timed alignments between SL motion capture data, text and hierarchical expressions from the framework called AZee at subsentential level. Animations representing new SL content are built from blocks of animations present in the corpus and adapted to the context if necessary. A smart blending approach has been designed that allows the concatenation, replacement and adaptation of original animation blocks. This approach has been tested on a tailored testset to show as a proof of concept its potential in comprehensibility and fluidity of the animation, as well as its current limits.