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<h1>Workshop Schedule</h1>

<p>The workshop will take place both in-person (in Lotus Suite 15) and online on <strong>August 16th, 2024.</strong> at ACL 2024.</p>
<table>
<thead>
<tr>
<th>Program</th>
<th>Time (Local)</th>
<th>Details</th>
</tr>
</thead>
<tbody>
<tr>
<td>Intro & Welcome</td>
<td>09:00 - 09:10</td>
<td></td>
</tr>
<tr class="keynote">
<td>Keynote 1</td>
<td>09:10 - 09:50</td>
<td>
Speaker: <i><a href="https://www.informatik.tu-darmstadt.de/ukp/ukp_home/staff_ukp/prof_dr_iryna_gurevych/index.en.jsp">Iryna Gurevych</a> (Technical University Darmstadt and head of the UKP Lab)</i>
<br>Title: <b>How to InterText? Elevating NLP to the cross-document level</b>
</td>
</tr>
<tr>
<td>Session #1</td>
<td>09:50 - 10:35</td>
<td>
<ul>
<li>Simulating Expert Discussions with Multi-agent for Enhanced Scientific Problem Solving
<br>Authors: Ziyue Li, Yuan Chang, Xiaoqiu Le
<br>Presenter: Ziyue Li (in-person)</li>
<li>CoSAEmb: Contrastive Section-aware Aspect Embeddings for Scientific Articles
<br>Authors: Shruti Singh, Mayank Singh
<br>Presenter: Shruti Singh (in-person)</li>
<li>AffilGood: Building reliable institution name disambiguation tools to improve scientific literature analysis
<br>Authors: Nicolau Duran-Silva, Pablo Accuosto, Piotr Przybyła, Horacio Saggion (in-person)</li>
</ul>
</td>
</tr>
<tr class="break">
<td>Break 1 + Networking</td>
<td>10:35 - 11:00</td>
<td></td>
</tr>
<tr class="keynote">
<td>Keynote 2</td>
<td>11:00 - 11:40</td>
<td>
Speaker: <i><a href="https://www.cs.northwestern.edu/~ddowney/">Doug Downey</a> (Northwestern University and Allen Institute for AI, USA)</i>
<br>Title: <b>Chasing high-precision NLP at discount prices: Lessons for accelerating science</b>
</td>
</tr>
<tr>
<td>Shared Tasks</td>
<td>11:40 - 12:10</td>
<td>2 overview talks:
<ul>
<li>DAGPap24: Detecting Automatically Generated Scientific Papers</li>
<li>Context24: Contextualizing Scientific Figures and Tables</li>
</ul>
</td>
</tr>
<tr>
<td>Session #2</td>
<td>12:10 - 12:40</td>
<td>
<ul>
<li>An Analysis of Tasks and Datasets in Peer Reviewing
<br>Authors: Moritz Staudinger, Wojciech Kusa, Florina Piroi, Allan Hanbury
<br>Presenter: Moritz Staudinger</li>
<li>Controllable Citation Sentence Generation with Language Models
<br>Authors: Nianlong Gu, Richard Hahnloser
<br>Presenter: Nianlong Gu (remote)</li>
</ul>
</td>
</tr>
<tr>
<tr class="poster-session">
<td>Lunch (+In-person Posters)</td>
<td>12:40 - 14:10</td>
<td><br>
In-person posters:
<ul>
<li>Artificial Intuition: Efficient Classification of Scientific Abstracts</li>
<li>Zero-shot Scientific Claim Verification Using LLMs and Citation Text</li>
<li>First Steps in Building a Knowledge Base of Mathematical Results</li>
<li>CiteAssist: A System for Automated Preprint Citation and BibTeX Generation</li>
<li>Researcher Representations Based on Aggregating Embeddings of Publication Titles: A Case Study in a Japanese Academic Database</li>
<li>CSIRO-LT at Context24: Contextualising Scientific Figures and Tables in Scientific Literature</li>
<li>OSX at Context24: How Well Can GPT Tackle Contexualizing Scientific Figures and Tables</li>
<li>Guiding Large Language Models via External Attention Prompting for Scientific Extreme Summarization</li>
<li>MISTI: Metadata-Informed Scientific Text and Image Representation through Contrastive Learning</li>
<li>Synthetic Context with LLM for Entity Linking from Scientific Tables</li>
<li>Beyond Retrieval: Topic-based Alignment of Scientific Papers to Research Proposal</li>
<li>AutoRef: Generating Refinements of Reviews Given Guidelines</li>
<li>Toward Related Work Generation with Structure and Novelty Statement</li>
</ul>
</td>
</tr>
</tr>
<tr class="keynote">
<td>Keynote 3</td>
<td>14:10 - 14:50</td>
<td>
Speaker: <i><a href="http://blender.cs.illinois.edu/hengji.html">Heng Ji</a> (University of Illinois at Urbana-Champaign, USA)</i>
<br>Title: <b>AI Plays Medicinal Chemist</b>
</td>
</tr>
<tr>
<td>Poster Session (Virtual in Gather)</td>
<td>14:50 - 15:30</td>
<td>Remote posters:
<ul>
<li>Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning</li>
<li>Papilusion at DAGPap24: Paper or Illusion? Detecting AI-generated Scientific Papers</li>
<li>Harnessing CLIP for Evidence Identification in Scientific Literature: A Multimodal Approach to Context24 Shared Task</li>
<li>An end-to-end entity recognition and disambiguation framework for identifying Author Affiliation from literature publications</li>
<li>Utilizing an Ensemble Model with Anomalous Label Smoothing to Detect Generated Scientific Papers</li>
<li>Cited Text Spans for Scientific Citation Text Generation</li>
<li>Multi-head Span-based Detector for AI-generated Fragments in Scientific Papers</li>
</ul>
</td>
</tr>
<tr class="break">
<td>Break 2 + Networking</td>
<td>15:30 - 16:00</td>
<td></td>
</tr>
<tr class="keynote">
<td>Keynote 4</td>
<td>16:00 - 16:40</td>
<td>
Speaker: <i><a href="https://annargrs.github.io/">Anna Rogers</a> (University of Copenhagen)</i>
<br>Title: <b>Large language models as research assistants: workflows and challenges</b>
</td>
</tr>
<tr>
<td>Session #3</td>
<td>16:40 - 17:25</td>
<td>
<ul>
<li>Metadata Enhancement Using Large Language Models
<br>Authors: Hyunju Song, Steven Bethard, Andrea Thomer
<br>Presenter: Hyunju Song (remote)</li>
<li>Integrating Table Representations into Large Language Models for Improved Scholarly Document Comprehension
<br>Authors: Buse Sibel Korkmaz, Antonio del Rio Chanona
<br>Presenter: Buse Sibel Korkmaz</li>
<li>Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
<br>Authors: Sai Munikoti, Anurag Acharya, Sridevi Wagle, Sameera Horawalavithana
<br>Presenter: Sai Munikoti (remote)</li>
</ul>
</td>
</tr>
<tr>
<td>Closing</td>
<td>17:25 - 17:30</td>
<td></td>
</tr>
</tbody>
</table>
<table class="table program">
<thead>
<tr>
<th>Program</th>
<th>Time (Local)</th>
<th>Details</th>
</tr>
</thead>
<tbody>
<tr>
<td>Intro & Welcome</td>
<td>09:00 - 09:10</td>
<td></td>
</tr>
<tr class="keynote">
<td>Keynote 1</td>
<td>09:10 - 09:50</td>
<td>
Speaker: <i><a href="https://www.informatik.tu-darmstadt.de/ukp/ukp_home/staff_ukp/prof_dr_iryna_gurevych/index.en.jsp">Iryna Gurevych</a> (Technical University Darmstadt and head of the UKP Lab)</i>
<br>Title: <b>How to InterText? Elevating NLP to the cross-document level</b>
</td>
</tr>
<tr class="oral-talks">
<td>Oral Talks 1</td>
<td>09:50 - 10:35</td>
<td>3 talks:
<ul>
<li>Simulating Expert Discussions with Multi-agent for Enhanced Scientific Problem Solving</li>
<li>CoSAEmb: Contrastive Section-aware Aspect Embeddings for Scientific Articles</li>
<li>AffilGood: Building reliable institution name disambiguation tools to improve scientific literature analysis</li>
</ul>
</td>
</tr>
<tr class="break">
<td>Break 1 + Networking</td>
<td>10:35 - 11:00</td>
<td></td>
</tr>
<tr class="keynote">
<td>Keynote 2</td>
<td>11:00 - 11:40</td>
<td>
Speaker: <i><a href="https://www.cs.northwestern.edu/~ddowney/">Doug Downey</a> (Northwestern University and Allen Institute for AI, USA)</i>
<br>Title: <b>Chasing high-precision NLP at discount prices: Lessons for accelerating science</b>
</td>
</tr>
<tr>
<td>Shared Tasks</td>
<td>11:40 - 12:10</td>
<td>2 overview talks
<ul>
<li>DAGPap24: Detecting Automatically Generated Scientific Papers</li>
<li>Context24: Contextualizing Scientific Figures and Tables</li>
</ul>
</td>
</tr>
<tr class="oral-talks">
<td>Oral Talks 2</td>
<td>12:10 - 12:40</td>
<td>2 talks:
<ul>
<li>An Analysis of Tasks and Datasets in Peer Reviewing (in-person)</li>
<li>Controllable Citation Sentence Generation with Language Models (remote)</li>
</ul>
</td>
</tr>
<tr class="poster-session">
<td>Lunch (+In-person Posters)</td>
<td>12:40 - 14:10</td>
<td><br>
In-person posters:
<ul>
<li>Artificial Intuition: Efficient Classification of Scientific Abstracts</li>
<li>Zero-shot Scientific Claim Verification Using LLMs and Citation Text</li>
<li>First Steps in Building a Knowledge Base of Mathematical Results</li>
<li>CiteAssist: A System for Automated Preprint Citation and BibTeX Generation</li>
<li>Researcher Representations Based on Aggregating Embeddings of Publication Titles: A Case Study in a Japanese Academic Database</li>
<li>CSIRO-LT at Context24: Contextualising Scientific Figures and Tables in Scientific Literature</li>
<li>OSX at Context24: How Well Can GPT Tackle Contexualizing Scientific Figures and Tables</li>
<li>Guiding Large Language Models via External Attention Prompting for Scientific Extreme Summarization</li>
<li>MISTI: Metadata-Informed Scientific Text and Image Representation through Contrastive Learning</li>
<li>Synthetic Context with LLM for Entity Linking from Scientific Tables</li>
<li>Beyond Retrieval: Topic-based Alignment of Scientific Papers to Research Proposal</li>
<li>AutoRef: Generating Refinements of Reviews Given Guidelines</li>
<li>Toward Related Work Generation with Structure and Novelty Statement</li>
</ul>
</td>
</tr>
<tr class="keynote">
<td>Keynote 3</td>
<td>14:10 - 14:50</td>
<td>
Speaker: <i><a href="http://blender.cs.illinois.edu/hengji.html">Heng Ji</a> (University of Illinois at Urbana-Champaign, USA)</i>
<br>Title: <b>AI Plays Medicinal Chemist</b>
</td>
</tr>
<tr class="poster-session">
<td>Poster Session (Virtual in Gather)</td>
<td>14:50 - 15:30</td>
<td>Remote posters:
<ul>
<li>Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning</li>
<li>Papilusion at DAGPap24: Paper or Illusion? Detecting AI-generated Scientific Papers</li>
<li>Harnessing CLIP for Evidence Identification in Scientific Literature: A Multimodal Approach to Context24 Shared Task</li>
<li>An end-to-end entity recognition and disambiguation framework for identifying Author Affiliation from literature publications</li>
<li>Utilizing an Ensemble Model with Anomalous Label Smoothing to Detect Generated Scientific Papers</li>
<li>Cited Text Spans for Scientific Citation Text Generation</li>
<li>Multi-head Span-based Detector for AI-generated Fragments in Scientific Papers</li>
</ul>
</td>
</tr>
<tr class="break">
<td>Break 2 + Networking</td>
<td>15:30 - 16:00</td>
<td></td>
</tr>
<tr class="keynote">
<td>Keynote 4</td>
<td>16:00 - 16:40</td>
<td>
Speaker: <i><a href="https://annargrs.github.io/">Anna Rogers</a> (University of Copenhagen)</i>
<br>Title: <b>Large language models as research assistants: workflows and challenges</b>
</td>
</tr>
<tr class="oral-talks">
<td>Oral Talks 3</td>
<td>16:40 - 17:25</td>
<td>3 talks:
<ul>
<li>Metadata Enhancement Using Large Language Models (remote)</li>
<li>Integrating Table Representations into Large Language Models for Improved Scholarly Document Comprehension (in-person)</li>
<li>Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning (remote)</li>
</ul>
</td>
</tr>
<tr>
<td>Closing</td>
<td>17:25 - 17:30</td>
<td></td>
</tr>
</tbody>
</table>


<!-- FOOTER ========================================== -->
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