Summarization in the Financial and Regulatory Domain

2020 ◽  
pp. 187-215 ◽  
Author(s):  
Jochen L. Leidner
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Entry Point ◽  
Special Consideration ◽  
Regulatory Domain ◽  
Automatic Summarization ◽  
Regulatory Domains

This chapter presents an introduction to automatic summarization techniques with special consideration of the financial and regulatory domains. It aims to provide an entry point to the field for readers interested in natural language processing (NLP) who are experts in the finance and/or regulatory domain, or to NLP researchers who would like to learn more about financial and regulatory applications. After introducing some core summarization concepts and the two domains are considered, some key methods and systems are described. Evaluation and quality concerns are also summarized. To conclude, some pointers for future reading are provided.

scholarly journals Text Mining Business Policy Documents

2020 ◽  
Vol 11 (2) ◽  
pp. 28-46 ◽  
Author(s):  
Marco Spruit ◽  
Drilon Ferati
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Qualitative Assessment ◽  
Keyword Extraction ◽  
Automatic Summarization ◽  
Business Policy ◽  
Policy Documents ◽  
Modelling Framework ◽  
Processing Techniques

In a time when the employment of natural language processing techniques in domains such as biomedicine, national security, finance, and law is flourishing, this study takes a deep look at its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the authors implement a set of natural language processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiments and expert evaluation, introduce a meta-algorithmic modelling framework for processing internal business policies. This framework relies on three natural language processing techniques, namely information extraction, automatic summarization, and automatic keyword extraction. For the reference extraction and keyword extraction tasks, the authors calculated precision, recall, and F-scores. For the former, the researchers obtained 0.99, 0.84, and 0.89; for the latter, this research obtained 0.79, 0.87, and 0.83, respectively. Finally, the summary extraction approach was positively evaluated using a qualitative assessment.

scholarly journals Linguistic challenges in automatic summarization technology

2017 ◽  
Vol 1 (1) ◽  
pp. 40
Author(s):  
Elke Diedrichsen
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Video Summarization ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Automatic Summarization ◽  
Document Summarization ◽  
Part Of Speech ◽  
Linguistic Challenges

Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

Sign in / Sign up

Export Citation Format

Share Document