Renarration for All

TL;DR

This paper proposes a human-in-the-loop approach for renarrating Web content to improve accessibility and inclusiveness, addressing language, literacy, and cultural barriers through transformations like simplification and translation.

Contribution

It introduces a model and architecture for renarration, leveraging Web Annotation Data Model to support inclusive, decentralized content adaptation.

Findings

Model and architecture for renarration presented

Potential of Web Annotation Data Model discussed

Enhances content accessibility for diverse populations

Abstract

The accessibility of content for all has been a key goal of the Web since its conception. However, true accessibility -- access to relevant content in the global context -- has been elusive for reasons that extend beyond physical accessibility issues. Among them are the spoken languages, literacy levels, expertise, and culture. These issues are highly significant, since information may not reach those who are the most in need of it. For example, the minimum wage laws that are published in legalese on government sites and the low-literate and immigrant populations. While some organizations and volunteers work on bridging such gaps by creating and disseminating alternative versions of such content, Web scale solutions much be developed to take advantage of its distributed dissemination capabilities. This work examines content accessibility from the perspective of inclusiveness. For this purpose, a human in the loop approach for renarrating Web content is proposed, where a renarrator creates an alternative narrative of some Web content with the intent of extending its reach. A renarration relates some Web content with an alternative version by means of transformations like simplification, elaboration, translation, or production of audio and video material. This work presents a model and a basic architecture for supporting renarrations along with various scenarios. We also discuss the potentials of the W3C specification for Web Annotation Data Model towards a more inclusive and decentralized social web.