Update sharedtasks.html

2025/sharedtasks.html

@@ -87,6 +87,15 @@ <h2 id="dagpap25">DAGPap25: Detecting automatically generated scientific papers<
         <p>
           A big problem with the ubiquity of Generative AI is that it has now become very easy to generate fake scientific papers. This can erode public trust in science and attack the foundations of science: are we standing on the shoulders of robots? The Detecting Automatically Generated Papers (DAGPAP) competition aims to encourage the development of robust, reliable AI-generated scientific text detection systems, utilizing a diverse dataset and varied machine learning models in a number of scientific domains. 
         </p> 
+
+		<h3>Organizers</h3>
+
+        <p><a href="https://www.linkedin.com/in/savvas-chamezopoulos-a567038a">Savvas Chamezopoulos</a>, Elsevier</p>
+
+
+        <p><a href="">Dan Li</a></p>
+
+        <p><a href="https://www.elsevier.com/connect/contributors/anita-de-waard-phd">Anita de Waard</a>, Elsevier</p>
       <!--   <p>
           <!-- You are invited to participate in the shared task "DAGPap24: Detecting automatically generated scientific papers" collocated with the <a href="https://sdproc.org/2024/">4th Workshop on 
           Scholarly Document Processing</a> (SDP 2024) to be held at <a href="https://2024.aclweb.org/">ACL 2024</a>. The competition will be held on <a href="https://www.codabench.org/">CodaBench</a>, 
@@ -211,6 +220,12 @@ <h2 id="context25">Context25: Contextualizing Scientific Figures and Tables </h2
 	     <p>
           Interpreting scientific claims in the context of empirical findings is a valuable practice, yet extremely time-consuming for researchers. Such interpretation of scientific claims requires identifying key results that provide supporting evidence from research papers, and contextualizing these results with associated methodological details (e.g., measures, sample, etc.). In this shared task, we are interested in automating identification of key results (or evidence) as well as additional grounding context to make claim interpretation more efficient. 
         </p>
+
+		         <h3>Organizers</h3>
+
+        <p><a href="">Joel Chan (University of Maryland)</a></p>
+        <p><a href="">Matthew Akamatsu (University of Washington)</a></p>
+        <p><a href="">Aakanksha Naik (Allen Institute for AI)</a></p>
 
        <!-- <p>
         All papers must follow the ACL format and conform to the ACL 2024 Submission Guidelines. Papers must be submitted via <a href="https://openreview.net/group?id=aclweb.org/ACL/2024/Workshop/SDProc">openreview</a>.
@@ -263,11 +278,7 @@ <h3>Important Dates (Shared Task)</h3>
             <li>Workshop dates: August 15th–16th, 2024 </li>
           </ul> 
 
-         <h3>Organizers</h3>
-
-        <p><a href="">Joel Chan (University of Maryland)</a></p>
-        <p><a href="">Matthew Akamatsu (University of Washington)</a></p>
-        <p><a href="">Aakanksha Naik (Allen Institute for AI)</a></p> -->
+ -->
 
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