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<p class="MsoNormal">This is an announcement of Karen Zhou's MS Presentation</p>
<p class="MsoNormal">===============================================</p>
<p class="MsoNormal">Candidate: Karen Zhou </p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Date: November 16,2023</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Time: 2:30pm</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Remote Location: <a href="https://urldefense.com/v3/__https:/uchicago.zoom.us/j/97199386376?pwd=cXVhell4SWhIWE9xYjJLc1lMd1NDdz09__;!!BpyFHLRN4TMTrA!5ooV0D41DvEGunuI32dvhyjhCCI0DKm90Rb21qcbTDkeHBmNNkjVfcjVTeMu1xn-O0I8opRriDNvk44s6S3mM379QBtBseU$">
https://urldefense.com/v3/__https://uchicago.zoom.us/j/97199386376?pwd=cXVhell4SWhIWE9xYjJLc1lMd1NDdz09__;!!BpyFHLRN4TMTrA!5ooV0D41DvEGunuI32dvhyjhCCI0DKm90Rb21qcbTDkeHBmNNkjVfcjVTeMu1xn-O0I8opRriDNvk44s6S3mM379QBtBseU$</a></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Location: JCL 346</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">M.S. Paper Title: Entity-Based Evaluation of Political Bias in Automatic Summarization</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Abstract: Growing literature has shown that NLP systems may encode social biases;</p>
<p class="MsoNormal">however, the political bias of summarization models remains relatively unknown. In this work, we use an entity replacement method to investigate the portrayal of politicians in automatically generated summaries of news articles. We develop
an entity-based computational framework to assess the sensitivities of several extractive and abstractive summarizers to the politicians Donald Trump and Joe Biden. We find consistent differences in these summaries upon entity replacement, such as reduced
emphasis of Trump's presence in the context of the same article and a more individualistic representation of Trump with respect to the collective US government (i.e., administration). These summary dissimilarities are most prominent when the entity is heavily
featured in the source article. Our characterization provides a foundation for future studies of bias in summarization and for normative discussions on the ideal qualities of automatic summaries.</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Advisors: Chenhao Tan</p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Committee Members: Chenhao Tan, Mina Lee, Ben Zhao</p>
<p class="MsoNormal"><o:p> </o:p></p>
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