Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with the standard pre-training corpora and finetuning datasets. Hence, the strong performance of PLMs may rely on the parametric knowledge that is memorized during pre-training and fine-tuning. Moreover, the knowledge memorized by PLMs may quickly become outdated, which affects the generalization performance of PLMs on future data. In this work, we propose TempoSum, a novel benchmark that contains data samples from 2010 to 2022, to understand the temporal generalization ability of abstractive summarization models. Through extensive human evaluation, we show that parametric knowledge stored in summarization models significantly affects the faithfulness of the generated summaries on future data. Moreover, existing faithfulness enhancement methods cannot reliably improve the faithfulness of summarization models on future data. Finally, we discuss several recommendations to the research community on how to evaluate and improve the temporal generalization capability of text summarization models.

The work presented in this paper is to propose a reliable high-quality system of Convolutional Neural Network (CNN) for brain tumor segmentation with a low computation requirement. The system consists of a CNN for the main processing for the segmentation, a pre-CNN block for data reduction and post-CNN refinement block. The unique CNN consists of 7 convolution layers involving only 108 kernels and 20308 trainable parameters. It is custom-designed, following the proposed paradigm of ASCNN (application specific CNN), to perform mono-modality and cross-modality feature extraction, tumor localization and pixel classification. Each layer fits the task assigned to it, by means of (i) appropriate normalization applied to its input data, (ii) correct convolution modes for the assigned task, and (iii) suitable nonlinear transformation to optimize the convolution results. In this specific design context, the number of kernels in each of the 7 layers is made to be just-sufficient for its task, instead of exponentially growing over the layers, to increase information density and to reduce randomness in the processing. The proposed activation function Full-ReLU helps to halve the number of kernels in convolution layers of high-pass filtering without degrading processing quality. A large number of experiments with BRATS2018 dataset have been conducted to measure the processing quality and reproducibility of the proposed system. The results demonstrate that the system reproduces reliably almost the same output to the same input after retraining. The mean dice scores for enhancing tumor, whole tumor and tumor core are 77.2%, 89.2% and 76.3%, respectively. The simple structure and reliable high processing quality of the proposed system will facilitate its implementation and medical applications.