Multilingual Financial Text Summarisation
Creators: name: Zmandar, Nadhem
Publisher: name: Lancaster University
Year: 2024

description: With the increasing growth in the number of public firms worldwide, the
 volume of financial disclosures and financial texts in different languages
 and forms is increasing sharply; therefore, the study of Natural Language
 Processing (NLP) methods that automatically summarise content has grown
 rapidly into a major research area. 
 
 
 Financial communication is a vital component of market transparency and
 constitutes a key element for investor’s confidence and the credibility
 and quality of a financial marketplace. Public firms are obliged to
 communicate regularly with their shareholders. The financial communication
 policy of a listed company reflects the regulatory constraints related to
 going public as well as the willingness of executives to regularly
 communicate with financial market players in a transparent, professional
 and responsive fashion. Financial narratives are used by firms to
 communicate with their stakeholders (investors, shareholders, customers,
 employees, financial analysts, regulators, lenders, rating agencies, and
 suppliers). Using financial communications,
 
 stakeholders could assess how the company can create value.
 
 
 This thesis explores the financial text summarisation task from different
 angles.
 
 The goal is the development of general and scalable algorithms that can
 jointly
 
 improve the state of the art (SOTA) of the tasks of financial text
 summarisation
 
 and compare different methodologies that combine quantitative and
 qualitative performance. The ability to extract key information from
 financial documents and generate summaries in multiple languages is
 crucial for financial professionals and organisations. However, current
 text summarisation methods cannot accurately identify and extract relevant
 information when applied to financial texts due to the domain-specific
 nature of the language, the differing structures of financial documents,
 the complexity of financial concepts and the lack of well-developed
 language resources and models. This study investigates how to adapt
 different transformer language models (general and domain-specific) and
 alternative unsupervised techniques to generate a coherent summary, then
 presents different ways to measure the performance
 
 by combining automatic and human evaluations, and finally it proposes
 several
 
 adversarial attacks and statistical methods to test the robustness of the
 results.
 
 The models in this thesis provide state-of-the-art performance on the
 multilingual financial summarisation task.
 
 
 This research contributes to the field of NLP by demonstrating our
 approach’s
 
 effectiveness in multilingual financial text summarisation and provides
 valuable
 
 insights for developing multilingual text summarisation systems. This
 thesis targets three languages: Arabic, French and English. It targets
 three financial reportingframeworks and three financial market cultures.
 It deals with three types of documents: long unstructured documents
 (English reports), medium structured reports (French reports) and
 financial newswires (Arabic). In addition, this thesis combines several
 novel contribution types: dataset creation, ontology labelling, benchmarks
 for financial summarisation systems, monitoring of the NLP training
 process and pretraining of novel language models to fill the lack of
 domain and language-specific language models. This thesis also presents a
 novel approach to automatically summarising long financial text in
 multiple languages. Using advanced pretrained transformers, our system can
 accurately identify and extract essential information from financial
 documents and generate extractive and abstractive summaries in various
 languages.