<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Abstractive Text Summarization with Sequence-to-Sequence Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1812.02303/</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/1812.02303/feed.xml" rel="self" type="application/rss+xml" /><item><title>Neural Abstractive Text Summarization with Sequence-to-Sequence Models — mentioned by applecrazy</title><link>https://news.ycombinator.com/item?id=20551119</link><guid isPermaLink="false">urn:hackerpapers:mention:1812.02303:20551119</guid><pubDate>Mon, 29 Jul 2019 02:38:53 GMT</pubDate><description>&lt;p&gt;&amp;gt; I don&amp;#x27;t think there is an ML which could extract the take aways from each section There&amp;#x27;s plenty of abstractive and extractive methods for text summarization. In fact, that&amp;#x27;s what I&amp;#x27;m working on right now, is to develop a news article summarization system for longer articles. If you&amp;#x27;re interested in learning more, there&amp;#x27;s an excellent survey paper on ArXiv: https://arxiv.org/abs/1812.02303&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1812.02303/"&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>
