The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. The Platform offers a fully automated media ingestion pipeline capable of recording live broadcasts, detection and transcription of spoken content, translation of all text (original or transcribed) into English, recognition and linking of Named Entities, topic detection, clustering and crosslingual multi-document summarization of related media items, and last but not least, extraction and storage of factual claims in these news items. Browser-based graphical user interfaces provide humans with aggregated information as well as structured access to individual news items stored in the Platform's database. This paper describes the intended use cases and provides an overview over the system's implementation.

Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation. In this paper, we present an automated fact checking platform which given a claim, it retrieves relevant textual evidence from a document collection, predicts whether each piece of evidence supports or refutes the claim, and returns a final verdict. We describe the architecture of the system and the user interface, focusing on the choices made to improve its user friendliness and transparency. We conduct a user study of the factchecking platform in a journalistic setting: we integrated it with a collection of news articles and provide an evaluation of the platform using feedback from journalists in their workflow. We found that the predictions of our platform were correct 58% of the time, and 59% of the returned evidence was relevant.

Pollination deficit could cause low yields in cashew (Anacardium occidentale) and it is possible that deforestation surrounding cashew plantations may prevent effective pollinators from visiting cashew flowers and contribute to this deficit. In the present work, we investigated the proximity effect of small and large forest fragments on the abundance and flower visits by feral Apis mellifera and wild native pollinators to cashew flowers and their interactions with yield in cashew plantations. Cashew nut yield was highest when plantations bordered a small forest fragment and were close to the large forest fragment. Yield from plantations that did not border small forest fragments but were close to the large forest fragment did not differ to yield from plantations at a greater distance to the large forest fragment. Flower visits by wild native pollinators, mainly Trigona spinipes, were negatively affected by distance to the large forest remnant and their numbers were directly correlated to nut yield. The number of A. mellifera visiting cashew flowers did not change significantly with distance to forest fragments, nor was it correlated with yield. We conclude that increasing the number of wild pollinator visits may increase yield, and proximity to large forest fragments are important for this.

In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in entity mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further propose a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.

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