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    <title>DEV Community: mitya8128</title>
    <description>The latest articles on DEV Community by mitya8128 (@mitya8128).</description>
    <link>https://dev.to/mitya8128</link>
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    <item>
      <title>text summarizer project</title>
      <dc:creator>mitya8128</dc:creator>
      <pubDate>Thu, 14 Oct 2021 20:54:26 +0000</pubDate>
      <link>https://dev.to/mitya8128/text-summarizer-project-59pd</link>
      <guid>https://dev.to/mitya8128/text-summarizer-project-59pd</guid>
      <description>&lt;p&gt;Hey, just want to drop a link to my &lt;a href="https://dev.to/mitya8128/graphsummarizer-h80"&gt;personal post&lt;/a&gt; about my new project in the field of text summarization&lt;/p&gt;

&lt;p&gt;Hope you'll enjoy =)&lt;/p&gt;

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      <category>python</category>
    </item>
    <item>
      <title>graph_summarizer</title>
      <dc:creator>mitya8128</dc:creator>
      <pubDate>Thu, 14 Oct 2021 20:42:14 +0000</pubDate>
      <link>https://dev.to/mitya8128/graphsummarizer-h80</link>
      <guid>https://dev.to/mitya8128/graphsummarizer-h80</guid>
      <description>&lt;div class="ltag-github-readme-tag"&gt;
  &lt;div class="readme-overview"&gt;
    &lt;h2&gt;
      &lt;img src="https://res.cloudinary.com/practicaldev/image/fetch/s--i3JOwpme--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev.to/assets/github-logo-ba8488d21cd8ee1fee097b8410db9deaa41d0ca30b004c0c63de0a479114156f.svg" alt="GitHub logo"&gt;
      &lt;a href="https://github.com/mitya8128"&gt;
        mitya8128
      &lt;/a&gt; / &lt;a href="https://github.com/mitya8128/graph_summarizer"&gt;
        graph_summarizer
      &lt;/a&gt;
    &lt;/h2&gt;
    &lt;h3&gt;
      summarize text using graphs and language vector models
    &lt;/h3&gt;
  &lt;/div&gt;
&lt;/div&gt;


&lt;p&gt;Today I want to tell about my new experimental project - text summarizer based on graph algorithms and distributive language models.&lt;/p&gt;

&lt;p&gt;Little prehistory: some time ago I worked on &lt;a href="https://github.com/mitya8128/nlp_graph"&gt;graph visualizations&lt;/a&gt; based on vector embedding of texts . This vizualisations show relational "distance" between words of sentences. So user can visualize graph of distances between word of text, also you can apply some advanced graph-theoretic approaches to further analyze input text and it's hidden features.&lt;br&gt;&lt;br&gt;
I want to use this approach to try on text summarization problem - in my case to extract some more informative sentences from text to compress original text.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/mitya8128/graph_summarizer"&gt;Here's&lt;/a&gt; the link to the repo&lt;/p&gt;

&lt;p&gt;You can use to play around, don't hesitate to contact me if you have any feedback!&lt;br&gt;
For local use you need to download &lt;a href="https://wikipedia2vec.github.io/wikipedia2vec/pretrained/"&gt;word2vec&lt;/a&gt; model (300d, binary format) and put it in &lt;em&gt;model&lt;/em&gt; folder inside root folder. &lt;br&gt;
&lt;a href="https://github.com/mitya8128/graph_summarizer/blob/master/demo_russian.ipynb"&gt;Demo of using&lt;/a&gt;  (yet only in Russian, soon I'll add another languages)   &lt;/p&gt;

&lt;p&gt;generate_summary_loop() function is an endpoint.&lt;br&gt;&lt;br&gt;
NB: use flag &lt;em&gt;need_tag=True&lt;/em&gt; if you need to use pretrained model with POS-tags, else &lt;em&gt;need_tag=False&lt;/em&gt;.&lt;/p&gt;

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      <category>python</category>
      <category>nlp</category>
      <category>opensource</category>
      <category>algorithms</category>
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