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<p class="MsoNormal"><span style="color:#212121">This is an announcement of Karen Zhou's MS Presentation<o:p></o:p></span></p>
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<span style="color:#212121">===============================================<o:p></o:p></span></p>
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<span style="color:#212121">Candidate: Karen Zhou<span class="apple-converted-space"> </span><o:p></o:p></span></p>
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<span style="color:#212121">Date: November 16,2023<o:p></o:p></span></p>
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<span style="color:#212121">Time: 2:30pm<o:p></o:p></span></p>
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<span style="color:#212121">Remote Location:<span class="apple-converted-space"> </span><a href="https://urldefense.com/v3/__https:/uchicago.zoom.us/j/97199386376?pwd=cXVhell4SWhIWE9xYjJLc1lMd1NDdz09__;!!BpyFHLRN4TMTrA!5ooV0D41DvEGunuI32dvhyjhCCI0DKm90Rb21qcbTDkeHBmNNkjVfcjVTeMu1xn-O0I8opRriDNvk44s6S3mM379QBtBseU$" title="https://urldefense.com/v3/__https:/uchicago.zoom.us/j/97199386376?pwd=cXVhell4SWhIWE9xYjJLc1lMd1NDdz09__;!!BpyFHLRN4TMTrA!5ooV0D41DvEGunuI32dvhyjhCCI0DKm90Rb21qcbTDkeHBmNNkjVfcjVTeMu1xn-O0I8opRriDNvk44s6S3mM379QBtBseU$"><span style="color:#0078D7">https://urldefense.com/v3/__https://uchicago.zoom.us/j/97199386376?pwd=cXVhell4SWhIWE9xYjJLc1lMd1NDdz09__;!!BpyFHLRN4TMTrA!5ooV0D41DvEGunuI32dvhyjhCCI0DKm90Rb21qcbTDkeHBmNNkjVfcjVTeMu1xn-O0I8opRriDNvk44s6S3mM379QBtBseU$</span></a><o:p></o:p></span></p>
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<span style="color:#212121">Location: JCL 346<o:p></o:p></span></p>
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<span style="color:#212121">M.S. Paper Title: Entity-Based Evaluation of Political Bias in Automatic Summarization<o:p></o:p></span></p>
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<span style="color:#212121">Abstract: Growing literature has shown that NLP systems may encode social biases;<o:p></o:p></span></p>
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<span style="color:#212121">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.<o:p></o:p></span></p>
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<span style="color:#212121">Advisors: Chenhao Tan<o:p></o:p></span></p>
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<span style="color:#212121">Committee Members: Chenhao Tan, Mina Lee, Ben Zhao<o:p></o:p></span></p>
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