Has some good links along with comprehensive background of how/why training data is collected.
Okay this is bold:
we believe that most conventional works in the field of text summarization are no longer necessary in the era of LLMs
While every other paper is like "oh boy yeah, LLMs have an awful hit rate for summarization." And yet:
As depicted in Table 1, humanwritten reference summaries exhibit either an equal or higher number of hallunications compared to GPT-4 summaries. In specific tasks such as multinews and code summarization, human-written summaries exhibit notably inferior factual consistency.
But! Also! Looks like the big issue with human-written summaries was "their lack of fluency," which sounds like the AI stuff was just written better? Guess that's valuable, especially in line with the supposed higher factuality of LLM-generate content.
Nearly 70% of newsroom staffers from a variety of backgrounds and organizations surveyed in December say they’re using the technology for crafting social media posts, newsletters and headlines; translation and transcribing interviews; and story drafts, among other uses. One-fifth said they’d used generative AI for multimedia, including social graphics and videos.