Background
Some of the potential designs for the Image Browsing feature include snippets of the surrounding text from wherever an article image was embedded on a given page.
We need to investigate how feasible it would be to build something like this. Wikipedia articles are notoriously unstructured, and are essentially "tag soup" that is meant to be understood by a human reader. But maybe there are some heuristics we can rely on to get reasonably-useful information most of the time?
We could consider both back-end and front-end approaches – a back-end approach might involve doing something with image metadata or parser output; a front-end approach might look like relying in jQuery or DOM elements to grab text near where an image was placed in the hopes that is relevant.
This investigation should be time-boxed to a single sprint, and we should be mindful about practical returns on time investment of any proposed approach here.
If there is no feasible solution (at least not one that we can fit into the scope of our MVP prototype) then we should drop this aspect of the design for now.
Requirements
- Investigate ways to get relevant and useful text content for images.
- Consider frontend and backend approaches.
- Document findings and file a follow-up task if needed.