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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning to Identify Ambiguous and Misleading News Headlines · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1705.06031/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1705.06031/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning to Identify Ambiguous and Misleading News Headlines — mentioned by bfirsh</title><link>https://news.ycombinator.com/item?id=15534583</link><guid isPermaLink="false">urn:hackerpapers:mention:1705.06031:15534583</guid><pubDate>Mon, 23 Oct 2017 16:49:47 GMT</pubDate><description>&lt;p&gt;We were frustrated by the experience of reading machine learning papers on screens (particularly phones/tablets). There are lots of good tools for authoring HTML papers (Distill, Authorea, etc) but nothing that deals with the vast number of PDF papers that already exist. So, we built Arxiv Vanity: a site that renders Arxiv papers as web pages. It’s still pretty janky, but for the papers that do render correctly, the experience is so much better than reading a PDF. For example: https://www.arxiv-vanity.com/papers/1705.04085v3/ https://www.arxiv-vanity.com/papers/1708.00884/…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1705.06031/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CL</category></item></channel></rss>
