Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page

Truly trapped rainbow by utilizing nonreciprocal waveguides

The concept of a "trapped rainbow" has generated considerable interest for optical data storage and processing. It aims to trap different frequency components of the wave packet at different positions permanently. However, all the previously proposed structures cannot truly achieve this effect, due to the difficulties in suppressing the reflection caused by strong intermodal coupling and distinguishing different frequency components simultaneously. In this article, we found a physical mechanism to achieve a truly "trapped rainbow" storage of electromagnetic wave. We utilize nonreciprocal waveguides under a tapered magnetic field to achieve this and such a trapping effect is stable even under fabrication disorders. We also observe hot spots and relatively long duration time of the trapped wave around critical positions through frequency domain and time domain simulations. The physical mechanism we found has a variety of potential applications ranging from wave harvesting and storage to nonlinearity enhancement.Comment: main manuscript: 14 pages, 6 figures. supplementary information: 3 pages, 2 figures, 2 movie