Assessing the Readability of Policy Documents on the Digital Single Market of the European Union

• URL: http://arxiv.org/abs/2102.11625v2
• Date: Wed, 15 Sep 2021 13:33:10 GMT
• Title: Assessing the Readability of Policy Documents on the Digital Single Market of the European Union
• Authors: Jukka Ruohonen
• Abstract summary: This paper evaluates the readability of 201 legislations and related policy documents in the European Union (EU) The empirical results indicate that (i) generally a Ph.D. level education is required to comprehend the DSM laws and policy documents. Although (ii) the results vary across the five indices used, (iii) readability has slightly improved over time.
• Score: 0.7106986689736826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Today, literature skills are necessary. Engineering and other technical professions are not an exception from this requirement. Traditionally, technical reading and writing have been framed with a limited scope, containing documentation, specifications, standards, and related text types. Nowadays, however, the scope covers also other text types, including legal, policy, and related documents. Given this motivation, this paper evaluates the readability of 201 legislations and related policy documents in the European Union (EU). The digital single market (DSM) provides the context. Five classical readability indices provide the methods; these are quantitative measures of a text's readability. The empirical results indicate that (i) generally a Ph.D. level education is required to comprehend the DSM laws and policy documents. Although (ii) the results vary across the five indices used, (iii) readability has slightly improved over time.

Related papers

• The Use of Readability Metrics in Legal Text: A Systematic Literature Review [3.439579933384111]
  Linguistic complexity is an important contributor to difficulties experienced by readers. Document readability metrics have been developed to measure document readability. Not all legal domains are well represented in terms of readability metrics.
  arXiv  Detail & Related papers  (2024-11-14T15:04:17Z)
• Text Classification using Graph Convolutional Networks: A Comprehensive Survey [11.1080224302799]
  Graph convolution network (GCN)-based approaches have gained a lot of traction in this domain over the last decade. This work aims to summarize and categorize various GCN-based Text Classification approaches with regard to the architecture and mode of supervision.
  arXiv  Detail & Related papers  (2024-10-12T07:03:42Z)
• Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges [16.35265384114857]
  The rapid advancements of Large Language Models (LLMs) have blurred the lines between human and machine authorship. This literature review serves a roadmap for researchers and practitioners interested in understanding the state of the art in this rapidly evolving field.
  arXiv  Detail & Related papers  (2024-08-16T17:58:49Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
  This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews. The newly emerging AI-generated literature reviews are also appraised. This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Innovative Methods for Non-Destructive Inspection of Handwritten Documents [0.0]
  We present a framework capable of extracting and analyzing intrinsic measures of manuscript documents using image processing and deep learning techniques. By quantifying the Euclidean distance between the feature vectors of the documents to be compared, authorship can be discerned. Experimental results demonstrate the ability of our method to objectively determine authorship in different writing media, outperforming the state of the art.
  arXiv  Detail & Related papers  (2023-10-17T12:45:04Z)
• Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs [65.9077733300329]
  Large Language Models (LLMs) have the potential to greatly enhance the analysis of public affairs documents. LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.
  arXiv  Detail & Related papers  (2023-06-05T13:35:01Z)
• Artificial intelligence technologies to support research assessment: A review [10.203602318836444]
  This literature review identifies indicators that associate with higher impact or higher quality research from article text. It includes studies that used machine learning techniques to predict citation counts or quality scores for journal articles or conference papers.
  arXiv  Detail & Related papers  (2022-12-11T06:58:39Z)
• An Inclusive Notion of Text [69.36678873492373]
  We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
  arXiv  Detail & Related papers  (2022-11-10T14:26:43Z)
• Open Set Classification of Untranscribed Handwritten Documents [56.0167902098419]
  Huge amounts of digital page images of important manuscripts are preserved in archives worldwide. The class or typology'' of a document is perhaps the most important tag to be included in the metadata. The technical problem is one of automatic classification of documents, each consisting of a set of untranscribed handwritten text images.
  arXiv  Detail & Related papers  (2022-06-20T20:43:50Z)
• A Survey of Deep Learning Approaches for OCR and Document Understanding [68.65995739708525]
  We review different techniques for document understanding for documents written in English. We consolidate methodologies present in literature to act as a jumping-off point for researchers exploring this area.
  arXiv  Detail & Related papers  (2020-11-27T03:05:59Z)
• SPECTER: Document-level Representation Learning using Citation-informed Transformers [51.048515757909215]
  SPECTER generates document-level embedding of scientific documents based on pretraining a Transformer language model. We introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction to document classification and recommendation.
  arXiv  Detail & Related papers  (2020-04-15T16:05:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.