AI 与写作2019news.mit.edu编辑精选
科学写作可以被自动化吗？
Can science writing be automated?
导读
这篇 MIT 报道讨论 AI 能否自动完成科学写作，尤其是把研究论文转成面向大众的科普文章。对写作者的价值在于：它逼我们区分“总结论文”和“写一篇有人愿意读的科普”，并反思自动化生成内容的边界。
可以直接用的写作要点
- 01
科普写作不是论文摘要：除了讲方法，还要补充语境、作者引语和“为什么重要”，让外行读者知道为何值得关心。
- 02
别把“论文进、文章出”当成写作公式；好科普需要选择角度、解释背景、激发兴趣，这些是摘要模型难以替代的。
- 03
少堆“深度/神经网络”等热词和夸大声明，标题与宣传要和实际贡献匹配，否则报道会失去可信度。
- 04
如果内容能被工具批量生成，它往往缺乏不可替代的附加值；写作者应提供采访、判断、语境等机器难以生成的东西。
HN 评论精选
科普文章需要引语、语境和激发兴趣，不能等同于论文摘要。
这篇文章本身恐怕不是简单把论文文本喂给模型就能生成的：它满是作者引语、评论和面向普通读者的语境解释（还夹着大学的自我宣传）。论文更偏技术方法，直接拿来写科普用处不大。科普写作的目标之一是激发读者兴趣、说明一个领域为何值得兴奋、让读者看到远处领域在发生什么，这远不是“论文进、科普出”。一句话：总结论文不等于写一篇有趣的科普文章。
bonoboTP查看英文原文
Could this very article have been written simply by applying some model to the paper text? I doubt it. Look at it, it's full of quotes from the authors, full of commentary and explaining the context to a lay audience (with a healthy dose of self-promotion from the side of the university). Although research papers do contain some context (on a more specialized level, and with many references to other papers instead of repeating the idea), they are more about the technical approach, which is less useful for direct use in the popular article. One goal of popular science writing is to inspire people, to tell us why we should be excited about a topic, to see what's going on in fields distant from ours etc. This is far from "research paper in -> pop sci article out". TL;DR: Summarizing a research paper is not the same as writing an interesting pop sci article about a research project.
批评炒作式科研报道：宣称应与实际贡献匹配，写作要克制夸大。
这些神经网络炒作文章，在目标、宣称和实际完成的事情之间差距很大，几乎只比 Buzzfeed 清单体高一级。套路是：列一堆雄心目标，找现成数据集（最好别让科学家真去收集和清洗新数据），用云计算把一堆神经网络架构扔上去，哪个有效就发论文；标题还得带“deep”，再配一篇用 Buzzfeed 文章训练出的神经网络写的公关稿。也许我说得有点狠，论文本身没有疯狂夸大，但这篇报道确实没帮上忙；如果研究报道是这种质量，那它倒不妨被自动化。
ssivark查看英文原文
Can deep learning paper writing be automated? :P These neural network hype articles are barely one step down from Buzzfeed listicles in what they project versus what they claim versus what they have actually done. Scan from a list of really ambitious goals, scan among list of available datasets (God forbid, no scientist should have to actually collect and clean new data pertaining to the problem!), throw a bunch of neural networks architectures at it leveraging cloud compute, and publish a paper based on whatever sticks. The process isn't complete without a title involving the word "deep" and a PR announcement generated by a neural network trained on Buzzfeed articles. Maybe I'm slightly too harsh. The actual paper in this case is not making crazy grandiose claims, but this article is certainly doing no favors. If this is the quality of research reporting, then it might as well be automated.
批量自动生成会稀释内容价值；可被工具生成的内容不值得读。
也许是我见太多自动生成、污染搜索结果的“文章”，我真希望这类东西别泛滥，否则只会制造更多垃圾，而且更难与有用内容区分。总的来说，我对它的看法类似自动生成的代码注释：“能被工具生成的东西，就不值得读。”
userbinator查看英文原文
Maybe it's because I've seen too much of the masses of automatically generated "articles" that pollute my search results, but I really hope stuff like this doesn't proliferate, because I think it'll just lead to even more of that spam --- and be even harder to separate from the useful stuff. In general, I have the same opinion of this as I do things like automatically generated comments in code: "If it can be generated by a tool, it's not worth reading."
自动生成可做到表面连贯，但连贯不等于有真正的研究或写作价值。
这让我想起 MIT 学生早先做的 SCIGen：输入作者名，它就能生成看似连贯的“研究论文”文本，他们甚至把它投到了会议。
eatbitseveryday查看英文原文
Reminds me of this earlier work from students also at MIT, called SCIGen [1]. You can type in author names and it’ll generate some seemingly coherent text for you as a “research paper”. They even submitted it to a conference. [1] https://pdos.csail.mit.edu/archive/scigen/
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