Given the task definition, example input & output, solve the new input case.
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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Output: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

New input case for you: A collection of scientific papers is often accompanied by tags:
keywords, topics, concepts etc., associated with each paper.
Sometimes these tags are human-generated, sometimes they are
machine-generated. We propose a simple measure of the consistency
of the tagging of scientific papers: whether these tags are
predictive for the citation graph links. Since the authors tend to
cite papers about the topics close to those of their publications, a
consistent tagging system could predict citations. We present an
algorithm to calculate consistency, and experiments with human- and
machine-generated tags. We show that augmentation, i.e. the combination
of the manual tags with the machine-generated ones, can enhance the
consistency of the tags. We further introduce cross-consistency,
the ability to predict citation links between papers tagged by
different taggers, e.g. manually and by a machine.
Cross-consistency can be used to evaluate the tagging quality when
the amount of labeled data is limited.
Output:
A good tagger gives similar tags to a given paper and the papers it cites