Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain and to AI papers, highlighting its broad utility. We make our data, model and search engine publicly available.

Identifying the quality of free-text arguments has become an important task in the rapidly expanding field of computational argumentation. In this work, we explore the challenging task of argument quality ranking. To this end, we created a corpus of 30,497 arguments carefully annotated for point-wise quality, released as part of this work. To the best of our knowledge, this is the largest dataset annotated for point-wise argument quality, larger by a factor of five than previously released datasets. Moreover, we address the core issue of inducing a labeled score from crowd annotations by performing a comprehensive evaluation of different approaches to this problem. In addition, we analyze the quality dimensions that characterize this dataset. Finally, we present a neural method for argument quality ranking, which outperforms several baselines on our own dataset, as well as previous methods published for another dataset.

Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem.We propose to represent such summaries as a small set of talking points, termed key points, each scored according to its salience.We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments.Furthermore, we found that a domain expert can often predict these key points in advance.We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task.We report empirical results for an extensive set of experiments with this dataset, showing promising performance.

An educated and informed consumption of media content has become a challenge in modern times.With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views.We suggest a novel task aiming to alleviate some of these concerns -that of detecting articles that most effectively counter the arguments -and not just the stance -made in a given text.We study this problem in the context of debate speeches.Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it.We provide a large dataset of 3685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community.We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research.All data collected during this work is freely available for research 1 .

When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence.Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect.Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments.The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert.Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data.Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews.An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.

Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.