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pens_model.html
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<!DOCTYPE html>
<html >
<head>
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<title>Model</title>
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<section class="header1 cid-sxicZeGPdQ" id="header16-1w">
<div class="container">
<div class="row justify-content-md-center">
<div class="col-md-10 align-center">
<h1 class="mbr-section-title mbr-bold pb-3 mbr-fonts-style display-2">A Generic Framework </h1>
<p class="mbr-text pb-3 mbr-fonts-style display-5">A generic framework for personalized news headline generation based on PENS </p>
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<div class="mbr-text col-12 mbr-fonts-style display-5 col-md-12"><p><strong>Background</strong></p></div>
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<p class="mbr-text mb-0 mbr-fonts-style display-7"><strong><br></strong><strong>HOW</strong> to construct a news headline that captures reader curiosity without entering the domain of click-bait?<br><br><strong>Personalized News Headline</strong><br></p>
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<div class="mbr-text col-12 mbr-fonts-style display-5 col-md-12"><p><strong>Model Framework</strong></p></div>
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<p class="mbr-text mb-0 mbr-fonts-style display-7"><strong><br></strong><strong>How to learn personalization? </strong><br><br>Personalized news recommendation is the matching between candidate news and user interests, where accurate news representations and user preferences are learned.<br><br></p>
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<p class="mbr-text mb-0 mbr-fonts-style display-7"><strong><br></strong><strong><br></strong><strong>How to inject the user preference into a news headline generator？</strong><br><br>Our proposed headline generator is a variant of transformer encoder and pointer network decoder, with three different manners to inject user preferences learned to the generator.<br><br>It can be considered as a sequential decision-making process. The reward is estimated by personalization, fluency and factualness.<br> <br><br><br></p>
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<div class="mbr-text col-12 mbr-fonts-style display-7 col-md-10"><p><span style="font-size: 1rem;"><strong>Firstly</strong>, we can see that every personalized news headline generation method can outperform non-personalized methods like PG. It might be that our proposed framework can generate personalized news headlines by incorporating user interests.</span></p><p><strong>Secondly</strong><span style="font-size: 1rem;">, we can find that user modeling makes a difference in generating personalized headlines.</span></p><p><strong>Thirdly</strong>, it is reported that the second way of injecting user interests gets the best performance on most of the user modeling methods.</p><p><span style="font-size: 1rem;">However, it still remains a large room for explorations on better injecting user representations into the generation process since the second way seems to be defective at some time.</span></p></div>
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About PENS </a><div class="dropdown-menu"><a class="text-black dropdown-item text-primary display-4" href="pens_model.html" aria-expanded="false">MODEL</a><a class="text-black dropdown-item text-primary display-4" href="pens_data.html" aria-expanded="false">DATA</a></div></li></ul>
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<div class="mbr-text col-12 mbr-fonts-style display-7 col-md-10"><p>We believe the reason is that PG is trained via supervised learning to maximize the log-likelihood of ground-truth news headlines. While our framework is trained via RL technique where coverage score is considered as an indicator to encourage the generation to be more complete. In addition, the exhibited cases show that our framework can produce user-specific news headlines in accordance with their individual interests reflected by historical click behaviors.</p></div>
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