chore: update publications

src/data/publications.bib

@@ -1,11 +1,19 @@
 @inproceedings{Bergmann2018ReCAPInformationRetrieval,
   title = {{{ReCAP}} - {{Information Retrieval}} and {{Case-Based Reasoning}} for {{Robust Deliberation}} and {{Synthesis}} of {{Arguments}} in the {{Political Discourse}}},
-  booktitle = {Lernen. {{Wissen}}. {{Daten}}. {{Analysen}}. ({{LWDA}} 2018)},
+  booktitle = {Proceedings of the {{Conference}} "{{Lernen}}, {{Wissen}}, {{Daten}}, {{Analysen}}"},
   author = {Bergmann, Ralph and Schenkel, Ralf and Dumani, Lorik and Ollinger, Stefan},
-  date = {2018},
+  editor = {Gemulla, Rainer and Ponzetto, Simone Paolo and Bizer, Christian and Keuper, Margret and Stuckenschmidt, Heiner},
+  date = {2018-08-22},
   series = {{{CEUR Workshop Proceedings}}},
-  url = {http://ceur-ws.org/Vol-2191/paper6.pdf},
-  abstract = {The ReCAP project is a recently started project within the DFG priority programm robust argumentation machines (RATIO). It follows the vision of future argumentation machines that support researchers, journalistic writers, as well as human decision makers to obtain a comprehensive overview of current arguments and opinions related to a certain topic, as well as to develop personal, well-founded opinions justified by convincing arguments. Unlike existing search engines, which primarily operate on the textual level, such argumentation machines will reason on the knowledge level formed by arguments and argumentation structures. The focus of ReCAP is on novel contributions to and confluence of methods from information retrieval and knowledge representation and reasoning, in particular case-based reasoning. The aim is to develop methods that are able to capture arguments in a robust and scalable manner, in particular representing, contextualizing, and aggregating arguments and making them available to a user. Together with experts from the political domain real-world scenarios and use cases are worked out. A corpus of semantically annotated argumentations is being created from relevant text sources and will be made available to the argumentation research community.}
+  volume = {2191},
+  pages = {49--60},
+  publisher = {CEUR},
+  location = {Mannheim, Germany},
+  issn = {1613-0073},
+  url = {https://ceur-ws.org/Vol-2191/#paper6},
+  urldate = {2024-12-11},
+  eventtitle = {Lernen, {{Wissen}}, {{Daten}}, {{Analysen}} 2018},
+  langid = {english}
 }
 
 @inproceedings{Bergmann2019SimilarityMeasuresCaseBased,
@@ -85,16 +93,25 @@ @inproceedings{Britner2023AQUAPLANEArgumentQuality
   url = {https://dl.acm.org/doi/10.1145/3583780.3614733},
   urldate = {2023-11-24},
   abstract = {In computational argumentation, so-called quality dimensions such as coherence or rhetoric are often used for ranking arguments. However, the literature often only predicts which argument is more persuasive, but not why this is the case. In this paper, we introduce AQUAPLANE, a transparent and easy-to-extend application that not only decides for a pair of arguments which one is more convincing with respect to a statement, but also provides an explanation.},
-  isbn = {9798400701245}
+  isbn = {979-8-4007-0124-5}
 }
 
 @inproceedings{Dumani2019GoodPremisesRetrieval,
   title = {Good {{Premises Retrieval}} via a {{Two-Stage Argument Retrieval Model}}},
-  booktitle = {Grundlagen von {{Datenbanken}}},
+  booktitle = {Proceedings of the 31st {{GI-Workshop Grundlagen}} von {{Datenbanken}}},
   author = {Dumani, Lorik},
-  date = {2019},
-  url = {http://ceur-ws.org/Vol-2367/paper_13.pdf},
-  abstract = {This paper presents a research plan for an implementation of a two-stage argument retrieval model that first finds similar claims for a given query claim and then in the next step retrieves clusters of similar premises in a ranked order. Computational argumentation is an emerging research area. An argument consists of a claim that is supported or attacked by at least one premise. Its intention is the persuasion of others to a certain standpoint. An important problem in this field is the retrieval of good premises for a given claim from a corpus of arguments. Given a claim, a first step of existing approaches is often to find other claims that are textually similar. Then, the similar claim’s premises can be retrieved. This paper presents a research plan for an implementation of a two-stage argument retrieval model that first finds similar claims for a given query claim and then in the next step retrieves clusters of similar premises in a ranked order.}
+  editor = {Schenkel, Ralf},
+  date = {2019-06-11},
+  series = {{{CEUR Workshop Proceedings}}},
+  volume = {2367},
+  pages = {3--8},
+  publisher = {CEUR},
+  location = {Saarburg, Germany},
+  issn = {1613-0073},
+  url = {https://ceur-ws.org/Vol-2367/#paper_13},
+  urldate = {2024-12-11},
+  eventtitle = {Grundlagen von {{Datenbanken}}},
+  langid = {english}
 }
 
 @inproceedings{Dumani2019SystematicComparisonMethods,
@@ -147,13 +164,18 @@ @inproceedings{Dumani2020QualityAwareRankingArguments
 
 @inproceedings{Dumani2020RankingArgumentsCombining,
   title = {Ranking {{Arguments}} by {{Combining Claim Similarity}} and {{Argument Quality Dimensions}}},
-  booktitle = {{{CLEF}}},
+  booktitle = {Working {{Notes}} of {{CLEF}} 2020 - {{Conference}} and {{Labs}} of the {{Evaluation Forum}}},
   author = {Dumani, Lorik and Schenkel, Ralf},
-  date = {2020},
+  editor = {Cappellato, Linda and Eickhoff, Carsten and Ferro, Nicola and Névéol, Aurélie},
+  date = {2020-09-22},
+  series = {{{CEUR Workshop Proceedings}}},
   volume = {2696},
-  publisher = {CEUR-WS.org},
-  url = {http://ceur-ws.org/Vol-2696/paper_174.pdf},
-  abstract = {In this paper we describe our submissions to the CLEF lab Touché, which addresses argument retrieval from a focused debate collection. Our approach consists of a two-step retrieval. Step one finds the most similar claims to a query. Step two ranks the directly tied premises by the count of their convincingness compared to other relevant premises, for which we aggregate the sum of three main argument quality dimensions. The final ranking consists of the product of the two components which are expressed as probabilities.},
+  publisher = {CEUR},
+  location = {Thessaloniki, Greece},
+  issn = {1613-0073},
+  url = {https://ceur-ws.org/Vol-2696/#paper_174},
+  urldate = {2024-12-11},
+  eventtitle = {{{CLEF}} 2020 {{Working Notes}}},
   langid = {english}
 }
 
@@ -199,10 +221,30 @@ @inproceedings{Dumani2021ReCAPCorpusCorpus
   author = {Dumani, Lorik and Biertz, Manuel and Witry, Alex and Ludwig, Anna-Katharina and Lenz, Mirko and Ollinger, Stefan and Bergmann, Ralph and Schenkel, Ralf},
   date = {2021-01},
   pages = {248--255},
+  publisher = {IEEE},
+  location = {Laguna Hills, CA, USA},
   issn = {2325-6516},
   doi = {10.1109/ICSC50631.2021.00083},
   abstract = {The automatic extraction of arguments from natural language texts is a highly researched area and more important than ever today, as it is nearly impossible to manually capture all arguments on a controversial topic in a reasonable amount of time. For testing different algorithms such as the retrieval of the best arguments, which are still in their infancy, gold standards must exist. An argument consists of a claim or standpoint that is supported or opposed by at least one premise. The generic term for a claim or premise is Argumentative Discourse Unit (ADU). The relationships between ADUs can be specified by argument schemes and can lead to large graphs. This paper presents a corpus of 100 argument graphs with about 2,500 ADUs in German, which is unique in its size and the utilisation of argument schemes. The corpus is built from natural language texts like party press releases and parliamentary motions on education policies in the German federal states. Each high-quality text is presented by an argument graph and created by the use of a modified version of the annotation tool OVA. The final argument graphs resulted by merging two previously independently annotated graphs based on detailed discussions.},
-  eventtitle = {{{IEEE}} 15th {{International Conference}} on {{Semantic Computing}} ({{ICSC}})}
+  eventtitle = {{{IEEE}} 15th {{International Conference}} on {{Semantic Computing}} ({{ICSC}})},
+  langid = {english}
+}
+
+@inproceedings{Heinisch2024TellMeWho,
+  title = {Tell Me Who You Are and {{I}} Tell You How You Argue: {{Predicting Stances}} and {{Arguments}} for {{Stakeholder Groups}}},
+  shorttitle = {Tell Me Who You Are and {{I}} Tell You How You Argue},
+  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{NAACL}} 2024},
+  author = {Heinisch, Philipp and Dumani, Lorik and Cimiano, Philipp and Schenkel, Ralf},
+  editor = {Duh, Kevin and Gomez, Helena and Bethard, Steven},
+  date = {2024-06},
+  pages = {1968--1982},
+  publisher = {Association for Computational Linguistics},
+  location = {Mexico City, Mexico},
+  doi = {10.18653/v1/2024.findings-naacl.128},
+  url = {https://aclanthology.org/2024.findings-naacl.128},
+  urldate = {2024-12-11},
+  abstract = {Argument mining has focused so far mainly on the identification, extraction, and formalization of arguments. An important yet unaddressedtask consists in the prediction of the argumentative behavior of stakeholders in a debate. Predicting the argumentative behavior in advance can support foreseeing issues in public policy making or help recognize potential disagreements early on and help to resolve them. In this paper, we consider the novel task of predicting the argumentative behavior of individual stakeholders. We present ARGENST, a framework that relies on a recommender-based architecture to predict the stance and the argumentative main point on a specific controversial topic for a given stakeholder, which is described in terms of a profile including properties related to demographic attributes, religious and political orientation, socio-economic background, etc. We evaluate our approach on the well-known debate.org dataset in terms of accuracy for predicting stance as well as in terms of similarity of the generated arguments to the ground truth arguments using BERTScore. As part of a case study, we show how juries of members representing different stakeholder groups and perspectives can be assembled to simulate the public opinion on a given topic.},
+  eventtitle = {Findings 2024}
 }
 
 @inproceedings{Lenz2019SemanticTextualSimilarity,
@@ -225,25 +267,25 @@ @inproceedings{Lenz2019SemanticTextualSimilarity
 
 @inproceedings{Lenz2020ArgumentMiningPipeline,
   title = {Towards an {{Argument Mining Pipeline Transforming Texts}} to {{Argument Graphs}}},
-  booktitle = {Proceedings of the 8th {{International Conference}} on {{Computational Models}} of {{Argument}}},
+  booktitle = {Computational {{Models}} of {{Argument}}},
   author = {Lenz, Mirko and Sahitaj, Premtim and Kallenberg, Sean and Coors, Christopher and Dumani, Lorik and Schenkel, Ralf and Bergmann, Ralph},
-  editor = {Prakken, Henry and Bistarelli, Stefano and Santini, Francesco and Taticchi, Carlo},
   date = {2020},
   series = {Frontiers in {{Artificial Intelligence}} and {{Applications}}},
   volume = {326},
-  eprint = {2006.04562},
-  eprinttype = {arXiv},
   pages = {263--270},
   publisher = {IOS Press},
-  location = {Perugia, Italy},
+  location = {Virtual Event},
   doi = {10.3233/FAIA200510},
+  url = {https://ebooks.iospress.nl/doi/10.3233/FAIA200510},
+  urldate = {2024-07-19},
   abstract = {This paper tackles the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of systems to provide a complete argumentative structure from arbitrary natural language text for general usage. We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations. We also introduce new methods for evaluating the performance based on existing benchmark argument structures. Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text.},
-  eventtitle = {Computational {{Models}} of {{Argument}}}
+  eventtitle = {International {{Conference}} on {{Computational Models}} of {{Argument}}},
+  langid = {english}
 }
 
 @inproceedings{Lenz2022ComparingUnsupervisedAlgorithms,
   title = {Comparing {{Unsupervised Algorithms}} to {{Construct Argument Graphs}}},
-  booktitle = {Joint {{Proceedings}} of {{Workshops}}, {{Tutorials}} and {{Doctoral Consortium}} Co-Located with the 45rd {{German Conference}} on {{Artificial Intelligence}}},
+  booktitle = {Joint {{Proceedings}} of {{Workshops}}, {{Tutorials}} and {{Doctoral Consortium}} Co-Located with the 45th {{German Conference}} on {{Artificial Intelligence}}},
   author = {Lenz, Mirko and Dumani, Lorik and Sahitaj, Premtim},
   editor = {Koert, Dorothea and Minor, Mirjam},
   date = {2022-09-19},
@@ -272,7 +314,9 @@ @inproceedings{Lenz2022UserCentricArgumentMining
   doi = {10.3233/FAIA220176},
   url = {https://ebooks.iospress.nl/doi/10.3233/FAIA220176},
   urldate = {2022-09-14},
-  abstract = {Existing tools to create argument graphs are tailored for experts in the domain of argumentation. By taking into account the needs of experts, laymen, and developers, we propose ArgueMapper as a novel argument diagramming tool and Arguebuf as its underlying format. ArgueMapper is the first of its kind to be optimized for mobile devices and provide a discoverable interface suitable for novice users. Arguebuf provides native implementations for all major programming languages via a code generation approach. To complement Arguebuf, we provide a supercharged Python implementation that enables advanced analysis. All of our contributions support AIF and are publicly available on GitHub under the MIT license.}
+  abstract = {Existing tools to create argument graphs are tailored for experts in the domain of argumentation. By taking into account the needs of experts, laymen, and developers, we propose ArgueMapper as a novel argument diagramming tool and Arguebuf as its underlying format. ArgueMapper is the first of its kind to be optimized for mobile devices and provide a discoverable interface suitable for novice users. Arguebuf provides native implementations for all major programming languages via a code generation approach. To complement Arguebuf, we provide a supercharged Python implementation that enables advanced analysis. All of our contributions support AIF and are publicly available on GitHub under the MIT license.},
+  eventtitle = {International {{Conference}} on {{Computational Models}} of {{Argument}}},
+  langid = {english}
 }
 
 @inproceedings{Lenz2022WorkshopTextMining,