This paper explores whether enhancing temporal reasoning capabilities in Large Language Models (LLMs) can improve the quality of timeline summarisation, the task of summarising long texts containing sequences of events, such as social media threads.We first introduce NarrativeReason, a novel dataset focused on temporal relations among sequential events within narratives, distinguishing it from existing temporal reasoning datasets that primarily address pair-wise event relations.Our approach then combines temporal reasoning with timeline summarisation through a knowledge distillation framework, where we first fine-tune a teacher model on temporal reasoning tasks and then distill this knowledge into a student model while simultaneously training it for the task of timeline summarisation.Experimental results demonstrate that our model achieves superior performance on out-of-domain mental health-related timeline summarisation tasks, which involve long social media threads with repetitions of events and a mix of emotions, highlighting the importance and generalisability of leveraging temporal reasoning to improve timeline summarisation.

Mobile Edge Computing (MEC) offloads service functionalities from central cloud to edge network and process user requests there, which reduces service latency and alleviates cloud burden. Only partial services can run on edge nodes with limited resource capacity. Both time varying and heterogeneity of services users requesting introduce great challenges for the resource utilization of edge nodes and user quality of service (QoS). Edge cooperation with joint optimization emerges to cope with this problem for MEC service provider. Recent researches focus on the non-cooperation or partial cooperation among edge nodes in local area network (LAN), their benefits are only explored on a small scale, and the users still face with resources waste and high service. This paper jointly optimizes service placing and task scheduling in MEC based on edge utility maximization and full cooperation of edge nodes in LAN. Edge full cooperation can place as many types of services as possible and capture more user requests in edge network so as to reduce the overall delay and edge energy consumption. Further considering the individual user QoS, we formularize the rewards in the edge utility to promote the local processing of user tasks. The joint optimization is a mixed integer nonlinear program problem which is NP-hard with high computational complexity. Therefore, we design a two-layer iterative strategy (TI-ST) based on Gibbs sampling and linear programming, which has polynomial computation complexity and has provably near optimal performance. Experimental results demonstrate the effectiveness of the proposed scheme when compared with the benchmark schemes.

Adam Tsakalidis, Federico Nanni, Anthony Hills, Jenny Chim, Jiayu Song, Maria Liakata. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary.Recent work has looked at opinion summarisation of clusters of social media posts.Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus of opinion summarisation.To address these challenges we present WassOS, an unsupervised abstractive summarization model which makes use of the Wasserstein distance.A Variational Autoencoder is used to get the distribution of documents/posts, and the distributions are disentangled into separate semantic and syntactic spaces.The summary distribution is obtained using the Wasserstein barycenter of the semantic and syntactic distributions.A latent variable sampled from the summary distribution is fed into a GRU decoder with a transformer layer to produce the final summary.Our experiments on multiple datasets including Twitter clusters, Reddit threads, and reviews show that WassOS almost always outperforms the state-of-the-art on ROUGE metrics and consistently produces the best summaries with respect to meaning preservation according to human evaluations.

Cross-modal hashing is a hot issue in the multimedia community, which is to generate compact hash code from multimedia content for efficient cross-modal search. Two challenges, i.e., (1) How to efficiently enhance cross-modal semantic mining is essential for cross-modal hash code learning, and (2) How to combine multiple semantic correlations learning to improve the semantic similarity preserving, cannot be ignored. To this end, this paper proposed a novel end-to-end cross-modal hashing approach, named Multiple Semantic Structure-Preserving Quantization (MSSPQ) that is to integrate deep hashing model with multiple semantic correlation learning to boost hash learning performance. The multiple semantic correlation learning consists of inter-modal and intra-modal pairwise correlation learning and Cosine correlation learning, which can comprehensively capture cross-modal consistent semantics and realize semantic similarity preserving. Extensive experiments are conducted on three multimedia datasets, which confirms that the proposed method outperforms the baselines.

With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user–item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.

Session-based recommendation is to predict an anonymous user's next action based on the user's historical actions in the current session. However, the cold-start problem of limited number of actions at the beginning of an anonymous session makes it difficult to model the user's behavior, i.e., hard to capture the user's various and dynamic preferences within the session. This severely affects the accuracy of session-based recommendation. Although some existing meta-learning based approaches have alleviated the cold-start problem by borrowing preferences from other users, they are still weak in modeling the behavior of the current user. To tackle the challenge, we propose a novel cluster-based meta-learning model for session-based recommendation. Specially, we adopt a soft-clustering method and design a parameter gate to better transfer shared knowledge across similar sessions and preserve the characteristics of the session itself. Besides, we apply two self-attention blocks to capture the transition patterns of sessions in both item and feature aspects. Finally, comprehensive experiments are conducted on two real-world datasets and demonstrate the superior performance of CBML over existing approaches.

The main challenge of cross-modal retrieval is how to efficiently realize semantic alignment and reduce the heterogeneity gap. However, existing approaches ignore the multi-grained semantic knowledge learning from different modalities. To this end, this paper proposes a novel end-to-end cross-modal representation method, termed as Multi-Graph based Hierarchical Semantic Fusion (MG-HSF). This method is an integration of multi-graph hierarchical semantic fusion with cross-modal adversarial learning, which captures fine-grained and coarse-grained semantic knowledge from cross-modal samples, and generate modalities-invariant representations in a common subspace. To evaluate the performance, extensive experiments are conducted on three benchmarks. The experimental results show that our method is superior than the state-of-the-arts.

The purpose of cross-modal retrieval is to find the relationship between different modal samples and to retrieve other modal samples with similar semantics by using a certain modal sample. As the data of different modalities presents heterogeneous low-level feature and semantic-related high-level features, the main problem of cross-modal retrieval is how to measure the similarity between different modalities. In this article, we present a novel cross-modal retrieval method, named Hybrid Cross-Modal Similarity Learning model (HCMSL for short). It aims to capture sufficient semantic information from both labeled and unlabeled cross-modal pairs and intra-modal pairs with same classification label. Specifically, a coupled deep fully connected networks are used to map cross-modal feature representations into a common subspace. Weight-sharing strategy is utilized between two branches of networks to diminish cross-modal heterogeneity. Furthermore, two Siamese CNN models are employed to learn intra-modal similarity from samples of same modality. Comprehensive experiments on real datasets clearly demonstrate that our proposed technique achieves substantial improvements over the state-of-the-art cross-modal retrieval techniques.

Ground-penetrating radar (GPR) has been widely used for landmine detection due to its high signal-to-noise ratio (SNR) and superior ability to image nonmetallic landmines. Processing GPR data to obtain better target images and to assist further object detection has been an active research area. Phase-shift migration is a widely used method; however, its wavenumber space is nonuniformly sampled because of the nonlinear relationship between the uniform frequency samples and the wavenumbers. Conventional methods use linear interpolation to obtain uniform wavenumber samples and compute the fast Fourier transform (FFT). This paper develops two- and three-dimensional migration methods that process GPR data to obtain images close to the actual target geometries using a nonuniform fast Fourier transform (NUFFT) algorithm. The proposed method is first compared to the conventional migration approaches on simulated data and then applied to landmine field data sets. Results suggest that the NUFFT migration method is useful in focusing images, estimating landmine structure, and retaining relatively high signal-to-noise ratio in the migrated data. The processed data sets are then fed to the normalized energy and least-mean-square-based anomaly detectors. Receiver operating characteristic curves of data sets processed by different migration methods are compared. The NUFFT migration shows potential improvements on both classifiers with a reduced false alarm rate at most probabilities of detection.