One of the fun parts of AI is finding out that abstractive summarization is "easy", but extractive summarization (which is what humans do far more often in prac

One of the fun parts of AI is finding out that abstractive summarization is "easy", but extractive summarization (which is what humans do far more often in practice) is still very hard. Partly because most datasets assume sentence level extractive summarization, which is often not how humans summarize documents. There's still tons of very low hanging fruit in the summarization work. I'm not aware of significant followup work to pointer networks besides pointer-generator networks, which these days are considered old news. Pointer based architectures are the ideal system for word level extractive summarizers, yet the very best extractive summarization systems today are usually nothing more than sentence selectors using some kinds of embeddings and cosine similarity. Happy to see such success with abstractive summaries, but the kind that myself and most other humans are interested in is still far from solved.