In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Example Input: We introduce a novel workflow, QCue, for providing textual stimulation during mind-mapping. Mind-mapping is a powerful tool whose intent is to allow one to externalize ideas and their relationships surrounding a central problem. The key challenge in mind-mapping is the difficulty in balancing the exploration of different aspects of the problem (breadth) with a detailed exploration of each of those aspects (depth). Our idea behind QCue is based on two mechanisms: (1) computer-generated automatic cues to stimulate the user to explore the breadth of topics based on the temporal and topological evolution of a mind-map and (2) user-elicited queries for helping the user explore the depth for a given topic. We present a two-phase study wherein the first phase provided insights that led to the development of our work-flow for stimulating the user through cues and queries. In the second phase, we present a between-subjects evaluation comparing QCue with a digital mind-mapping work-flow without computer intervention. Finally, we present an expert rater evaluation of the mind-maps created by users in conjunction with user feedback.
Example Output: This paper introduces a method to generate questions (cues) and queries (suggestions) to help users perform mind-mapping.

Example Input: Most algorithms for representation learning and link prediction in relational data have been designed for static data. However, the data they are applied to usually evolves with time, such as friend graphs in social networks or user interactions with items in recommender systems. This is also the case for knowledge bases, which contain facts such as (US, has president, B. Obama, [2009-2017]) that are valid only at certain points in time. For the problem of link prediction under temporal constraints, i.e., answering queries of the form (US, has president, ?, 2012), we propose a solution inspired by the canonical decomposition of tensors of order 4.
 We introduce new regularization schemes and present an extension of ComplEx that achieves state-of-the-art performance. Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.
Example Output: We propose new tensor decompositions and associated regularizers to obtain state of the art performances on temporal knowledge base completion.

Example Input:  Global feature pooling is a modern variant of feature pooling providing better interpretatability and regularization. Although alternative pooling methods exist (eg. max, lp norm, stochastic), the averaging operation is still the dominating global pooling scheme in popular models. As fine-grained recognition requires learning subtle, discriminative features, we consider the question: is average pooling the optimal strategy? We first ask: ``is there a difference between features learned by global average and max pooling?'' Visualization and quantitative analysis show that max pooling encourages learning features of different spatial scales. We then ask ``is there a single global feature pooling variant that's most suitable for fine-grained recognition?'' A thorough evaluation of nine representative pooling algorithms finds that: max pooling outperforms average pooling consistently across models, datasets, and image resolutions; it does so by reducing the generalization gap; and generalized pooling's performance increases almost monotonically as it changes from average to max. We finally ask: ``what's the best way to combine two heterogeneous pooling schemes?'' Common strategies struggle because of potential gradient conflict but the ``freeze-and-train'' trick works best. We also find that post-global batch normalization helps with faster convergence and improves model performance consistently.
Example Output:
A benchmark of nine representative global pooling schemes reveals some interesting findings.