*** Second Workshop on Information Extraction from Scientific Publications ( WIESP) at IJCNLP-AACL 2023 *** *** Website: https://ui.adsabs.harvard.edu/WIESP/2023/ *** *** Twitter: https://twitter.com/wiesp_nlp *** Building on the success of the First WIESP at AACL-IJCNLP 2022, the Second Workshop on Information Extraction from Scientific Publications (WIESP) will provide a platform to researchers to foster discussion and research on information extraction, mining, generation, and knowledge discovery from scientific publications using Natural Language Processing and Machine Learning techniques. A lot of technological change happened in one year (since the 1st WIESP), especially with Generative Artificial Intelligence research. We are incorporating a few additional topics to stay abreast with the latest developments and research in the community. The 2nd iteration of WIESP would focus on the following topics (but not limited to): - Large Language Models (LLMs) for Science - Application of LLMs on information extraction, generation, mining and knowledge discovery from scientific publications - Probing LLMs for scientific fact checking and misinformation - Scientific document parsing - Scientific named-entity recognition - Scientific article summarization - Question-answering on scientific articles - Citation context/span extraction - Structured information extraction from full-text, tables, figures, bibliography - Novel datasets curated from scientific publications - Argument extraction and mining - Challenges in information extraction from scientific articles - Building knowledge graphs via mining scientific literature; querying scientific knowledge graphs - Novel tools for IE on scientific literature and interaction with users - Mathematical information extraction - Scientific concepts, facts extraction - Visualizing scientific knowledge - Bibliometric and Altmetric studies via information extraction from scientific articles and metadata In addition to research paper presentations, WIESP will also feature keynote talks, a panel discussion on “Large Language Models and Scientific Literature Mining'', and shared tasks. We will update the details on our website as and when they become available. We especially welcome participation from academic and research institutions, government and industry labs, publishers, and information service providers. Projects and organizations using NLP/ML techniques in their text mining and enrichment efforts are also welcome to participate. We strongly encourage the participation of students, researchers, and science practitioners from diverse backgrounds, especially from underrepresented groups and communities, to be a part of WIESP events, and pro-actively make the workshop a diverse and inclusive one. ****Call for Papers**** We invite papers of the following categories: ***Long papers*** must describe substantial, original, completed, and unpublished work. Wherever appropriate, concrete evaluation and analysis should be included. Papers must not exceed eight (8) pages of content, plus unlimited pages of references. The final versions of long papers will be given one additional page of content (up to 9 pages) so that reviewers' comments can be taken into account. ***Short papers*** must describe original and unpublished work. Please note that a short paper is not a shortened long paper. Instead, short papers should have a point that can be made in a few pages, such as a small, focused contribution, a negative result, or an interesting application nugget. Short papers must not exceed four (4) pages, plus unlimited pages of references. The final versions of short papers will be given one additional page of content (up to 5 pages) so that reviewers' comments can be taken into account. In addition to papers, WIESP will also host shared tasks. More details on the WIESP shared tasks will be available on our website shortly. Also, we will publish separate CfPs on the shared tasks. Shared task authors will be invited to write their system descriptions and those will be subjected to peer review. ***Shared Task: Function of Citation in Astrophysics Literature (FOCAL)*** The citation graph is an essential tool for helping researchers find relevant literature. To further empower discovery, we aim to label the edges of the graph with the function of the citation: e.g. is the cited work necessary background knowledge, or is it used as a comparison, to the citing work? To start this process, we propose a shared task of automatically labelling citations with a function based on the textual context of the citation. A sample dataset and more instructions can be found at: https://ui.adsabs.harvard.edu/WIESP/2023/SharedTasks *All accepted papers would be published in the WIESP proceedings as part of IJCNLP-AACL 2023 and indexed in the ACL Anthology.* ***Important Dates*** - Paper Submission Deadline: *September 11, 2023 (final extended deadline)* - Notification of workshop paper/abstract acceptance: October 5, 2023 - Camera-ready Submission Deadline: October 12, 2023 - Workshop: November 1, 2023 (online) ***All submission deadlines are 11.59 pm UTC -12h ("Anywhere on Earth")*** ****Submission Website and Format**** Submission Link: https://softconf.com/ijcnlp2023/WorkshopWIESP2023/ Submission will be via softconf. Submissions should follow the ACLPUB formatting guidelines (https://acl-org.github.io/ACLPUB/formatting.html) and template files (https://github.com/acl-org/acl-style-files/tree/master). Submissions (Long and Short Papers) will be subject to a double-blind peer-review process. We follow the same policies as IJCNLP-AACL 2023 regarding anonymity, preprints and double submissions. ***Organizers*** - Tirthankar Ghosal, National Center for Computational Sciences| Oak Ridge National Laboratory, USA - Felix Grezes, Center for Astrophysics | Harvard & Smithsonian, USA - Thomas Allen, Center for Astrophysics | Harvard & Smithsonian, USA - Kelly Lockhart, Center for Astrophysics | Harvard & Smithsonian, USA - Alberto Accomazzi, Center for Astrophysics | Harvard & Smithsonian, USA -- +++++++++++++++++++++++++++++++++++ Tirthankar Ghosal https://member.acm.org/~tghosal ++++++++++++++++++++++++++++++++++++