Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research.Traditional metrics based on citation counts often miss the nuanced ways a paper contributes to its field.In this work, we propose a new task: generating nuanced, expressive, and time-aware impact summaries that capture both praise (confirmation citations) and critique (correction citations) through the evolution of fine-grained citation intents.We introduce an evaluation framework tailored to this task, showing moderate to strong human correlation on subjective metrics such as insightfulness.Expert feedback from professors reveals a strong interest in these summaries and suggests future improvements.Data and code are made available.1

In this paper, we release data about demographic information and outliers of communities of interest. Identified from Wiki-based sources, mainly Wikidata, the data covers 7.5k communities, e.g., members of the White House Coronavirus Task Force, and 345k subjects, e.g., Deborah Birx. We describe the statistical inference methodology adopted to mine such data. We release subject-centric and group-centric datasets in JSON format, as well as a browsing interface. Finally, we forsee three areas where this dataset can be useful: in social sciences research, it provides a resource for demographic analyses; in web-scale collaborative encyclopedias, it serves as an edit recommender to fill knowledge gaps; and in web search, it offers lists of salient statements about queried subjects for higher user engagement. The dataset can be accessed at: https://doi.org/10.5281/zenodo.7410436

Knowledge bases about commonsense knowledge i.e., CSKBs, are crucial in applications such as search and question answering. Prominent CSKBs mostly focus on positive statements. In this paper we show that materializing important negations increases the usability of CSKBs. We present Uncommonsense, a web portal to explore informative negations about everyday concepts: (i) in a research-focused interface, users get a glimpse into results-per-steps of the methodology; (ii) in a trivia interface, users can browse fun negative trivia about concepts of their choice; and (iii) in a query interface, users can submit triple-pattern queries with explicit negated relations and compare results with significantly less relevant answers from the positive-only baseline. It can be accessed at:https://uncommonsense.mpi-inf.mpg.de/.

Commonsense knowledge about everyday concepts is an important asset for AI applications, such as question answering and chatbots. Recently, we have seen an increasing interest in the construction of structured commonsense knowledge bases (CSKBs). An important part of human commonsense is about properties that do not apply to concepts, yet existing CSKBs only store positive statements. Moreover, since CSKBs operate under the open-world assumption, absent statements are considered to have unknown truth rather than being invalid. This paper presents the UNCOMMONSENSE framework for materializing informative negative commonsense statements. Given a target concept, comparable concepts are identified in the CSKB, for which a local closed-world assumption is postulated. This way, positive statements about comparable concepts that are absent for the target concept become seeds for negative statement candidates. The large set of candidates is then scrutinized, pruned and ranked by informativeness. Intrinsic and extrinsic evaluations show that our method significantly outperforms the state-of-the-art. A large dataset of informative negations is released as a resource for future research.

Databases about general-world knowledge, so-called knowledge bases (KBs), are important in applications such as search and question answering. Traditionally, although KBs use open world assumption, popular KBs only store positive information, but withhold from taking any stance towards statements not contained in them. In this demo, we show that storing and presenting noteworthy negative statements would be important to overcome current limitations in various use cases. In particular, we introduce the Wiki neg ata portal, a platform to explore negative statements for Wikidata entities, by implementing a peer-based ranking method for inferring interesting negations in KBs. The demo is available at http://d5demos.mpi-inf.mpg.de/negation.

General-purpose knowledge bases (KBs) are an important component of several data-driven applications. Pragmatically constructed from available web sources, these KBs are far from complete, which poses a set of challenges in curation as well as consumption. In this tutorial we discuss how completeness, recall and negation in DBs and KBs can be represented, extracted, and inferred. We proceed in 5 parts: (i) We introduce the logical foundations of knowledge representation and querying under partial closed-world semantics. (ii) We show how information about recall can be identified in KBs and in text, and (iii) how it can be estimated via statistical patterns. (iv) We show how interesting negative statements can be identified, and (v) how recall can be targeted in a comparative notion.