Unsupervised Extractive Summarization with Learnable Length Control Strategies

Unsupervised extractive summarization is an important technique in information extraction and retrieval. Compared with supervised method, it does not require high-quality human-labelled summaries for training and thus can be easily applied for documents with different types, domains or languages. Most of existing unsupervised methods including TextRank and PACSUM rely on graph-based ranking on sentence centrality. However, this scorer can not be directly applied in end-to-end training, and the positional-related prior assumption is often needed for achieving good summaries. In addition, less attention is paid to length-controllable extractor, where users can decide to summarize texts under particular length constraint. This paper introduces an unsupervised extractive summarization model based on a siamese network, for which we develop a trainable bidirectional prediction objective between the selected summary and the original document. Different from the centrality-based ranking methods, our extractive scorer can be trained in an end-to-end manner, with no other requirement of positional assumption. In addition, we introduce a differentiable length control module by approximating 0-1 knapsack solver for end-to-end length-controllable extracting. Experiments show that our unsupervised method largely outperforms the centrality-based baseline using a same sentence encoder. In terms of length control ability, via our trainable knapsack module, the performance consistently outperforms the strong baseline without utilizing end-to-end training. Human evaluation further evidences that our method performs the best among baselines in terms of relevance and consistency.Comment: accepted by AAAI202

Enhancing Coherence of Extractive Summarization with Multitask Learning

This study proposes a multitask learning architecture for extractive summarization with coherence boosting. The architecture contains an extractive summarizer and coherent discriminator module. The coherent discriminator is trained online on the sentence vectors of the augmented textual input, thus improving its general ability of judging whether the input sentences are coherent. Meanwhile, we maximize the coherent scores from the coherent discriminator by updating the parameters of the summarizer. To make the extractive sentences trainable in a differentiable manner, we introduce two strategies, including pre-trained converting model (model-based) and converting matrix (MAT-based) that merge sentence representations. Experiments show that our proposed method significantly improves the proportion of consecutive sentences in the extracted summaries based on their positions in the original article (i.e., automatic sentence-level coherence metric), while the goodness in terms of other automatic metrics (i.e., Rouge scores and BertScores) are preserved. Human evaluation also evidences the improvement of coherence and consistency of the extracted summaries given by our method.Comment: 11 pages, 4 figure

Second-Order Nonlinearity in Triangular Lattice Perforated Gold Film due to Surface Plasmas Resonance

We have studied the excitation second-order nonlinearity through a triangular lattice perforated gold film instead of square lattice in many papers. Under the excitation of surface plasmas resonance effect, the second order nonlinearity exists in the noncentrosymmetric split-ring resonators arrays. Reflection of fundamental frequency wave through a triangular lattice perforated gold film is obtained. We also described the second harmonic conversion efficiencies in the second order nonlinear optical process with the spectra. Moreover, the electric field distributions of fundamental frequency above the gold film region are calculated. The light propagation through the holes results in the enhancement of the second order nonlinearity including second harmonic generation as well as the sum (difference) frequency generation

Online Architecture Optimization for Deep Neural Networks

Online data streaming has become one of the most common data forms in the modern world, also people use online approaches to improve the efficiency for model training, which imposes a strong demand of developing hyper-parameter optimization or architecture adaptation techniques for online learning. The thesis contains four projects on this. The first project is about online parallel hyper-parameter optimization and model training on data streams. A framework called HyperTube is proposed for online hyper-parameter optimization given the limited computing resources. This study also introduces “micro-mini-batch training mechanism” to reuse the online data mini-batches in a relatively efficient way. The second study is online adaptation of activation functions, in which I propose a general combined form of flexible activation functions as well as three principles of choosing flexible activation component. Based on this, two novel flexible activation functions with bounded or unbounded outputs are developed. Also, two new regularisation terms based on assumptions as prior knowledge are proposed. The third study is about online learning rate adaptation, in which I investigate different levels of learning rate adaptation based on the framework of hyper-gradient descent. Based on this, I propose an optimization method that adaptively learns the combination weights for different levels of adaptive learning rates. In the fourth study, I introduce a growing mechanism for differentiable neural architecture search based on network morphism. It enables growing of the cell structures from small size towards large size ones with one-shot training. Two modes can be applied in integrating the growing and original pruning process. Also, a novel two-input backbone architecture is proposed for recurrent neural networks. The proposed methods are well supported by experiments and could contribute to future studies for improving the efficiency of deep learning methods

Fano Resonance of the Symmetry-Reduced Metal Bar Grating Structure

We demonstrate that Fano resonance and even multipole Fano resonance can be obtained in a symmetry-reduced structure composed of gold bars with different bar sizes or bar shapes on a layer of dielectric. There is a transparency window opened within the frequency region of the absorptive dipole resonance by metallic bars, as long as the narrow grating waveguide mode induced by reducing symmetry is coincided in spectrum with the dipole resonance such that a destructive interference happens between these two resonant modes. Line shape of the transmission spectra of the nanostructure can be modulated effectively by changing the size or shape of the series of metal bars. The results found can be useful in the design of novel optical device

High Absorption and Second-Harmonic Generation in Split Ring Resonator Multilayer Nanostructure

Second-harmonic generation in split ring resonator multilayer nanostructure is studied with the finite-difference time-domain (FDTD) method. The fundamental frequency wave and the second-harmonic generation at the resonant absorption wavelength are highly localized in the dielectric layer, and the absorption peak is sensitive to dielectric constant of the dielectric layer. Under the excitation of the plasmon resonances mode, the strong local field induces an expected increase of the second-harmonic generation with conversion efficiencies 10−6-10−7. The distributions of fundamental frequency electric field and second-harmonic electric field inside the central dielectric layer region are also shown

Predicting Climate Change Impact on the Habitat Suitability of the <i>Schistosoma</i> Intermediate Host <i>Oncomelania hupensis</i> in the Yangtze River Economic Belt of China

Oncomelania hupensis is the exclusive intermediary host of Schistosoma japonicum in China. The alteration of O. hupensis habitat and population distribution directly affects the safety of millions of individuals residing in the Yangtze River Economic Belt (YREB) and the ecological stability of Yangtze River Basin. Therefore, it is crucial to analyze the influence of climate change on the distribution of O. hupensis in order to achieve accurate control over its population. This study utilized the MaxEnt model to forecast possible snail habitats by utilizing snail distribution data obtained from historical literature. The following outcomes were achieved: The primary ecological factors influencing the distribution of O. hupensis are elevation, minimum temperature of the coldest month, and precipitation of wettest month. Furthermore, future climate scenarios indicate a decrease in the distribution area and a northward shift of the distribution center for O. hupensis; specifically, those in the upstream will move northeast, while those in the midstream and downstream will move northwest. These changes in suitable habitat area, the average migration distance of distribution centers across different climate scenarios, time periods, and sub-basins within the YREB, result in uncertainty. This study offers theoretical justification for the prevention and control of O. hupensis along the YREB