MRR

Woloszyn, Vinicius; Santos, Henrique D. P. dos; Wives, Leandro Krug; Becker, Karin

doi:10.1145/3106426.3106444

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…In particular, review snippets selected by Rule 1 with descriptive adjs, 2, and 3 have higher priority than those by Rule 1 withe evaluative adjs; further, reviews of the same priority are ranked by their helpfulness (votes by other customers). The next level lower are reviews that contain the query keywords but do not meet any rules, within which they are ranked by relevance based on the concept of graph centrality following prior work [37]. For the brief appearance description, we select the shortest sentence in the top-3 candidates corresponding to each visual attribute (where each visual attribute is used as a keyword) since it is advised not to use long text for image alt-text.…”

Section: Generating Answers and Image Descriptionsmentioning

confidence: 99%

Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users

Wang¹,

Chen

Zhang

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Figure 1: Revamp simplifes and reconstructs the original Amazon web page for Blind or Low Vision users' information seeking task to understand product details, especially the appearance. Using rule-based heuristics, Revamp extracts descriptive information from customer reviews to generate image descriptions (a), responses to users' queries (b) with a sentiment summary of all the relevant reviews (c) and original reviews sorted into a positive and a negative lists (d).

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Section: Generating Answers and Image Descriptionsmentioning

confidence: 99%