Word level extractive summarization?

[Hellisotherpeople]

How difficult would it be to turn your code into a word-level model? That is, to select the best words to include in the summary, instead of at the sentence level. This task would be extremely useful to people in the Competitive Debate community, who need to underline evidence based on a tag-line, and cannot include words not found in the text (making abstractive summarization impossible)

[shashiongithub]

The Idea presented here could be easily applied to word level extraction. I am not sure if you want to keep the convolutional sentence decoder (it will depend on the encoder if you want to model the input document as a seq of sentences or words). The decoder will simply tag one word at a time instead of a sentence.

[Hellisotherpeople]

I'm not especially picky on how it's done. Do you have any real interest in going further with this? I have a dataset of debate evidence which is highlighted and has abstractive side information to include. I also have a burning passion to get this exact model created

Maybe a paper could come out of it?

[shashiongithub]

Hi Allen, Sounds very interesting. However, I have moved from Edinburgh recently. I would be very happy to discuss with you in depth about any implementation issues.

[Hellisotherpeople]

Can I contact you via the email you provided here? I'd like to get a word level version of sidenet working.

[shashiongithub]

Sure no problem!