Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding

Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.Comment: Update baseline

Summarisation of weighted networks

Networks often contain implicit structure. We introduce novel problems and methods that look for structure in networks, by grouping nodes into supernodes and edges to superedges, and then make this structure visible to the user in a smaller generalised network. This task of finding generalisations of nodes and edges is formulated as network Summarisation'. We propose models and algorithms for networks that have weights on edges, on nodes or on both, and study three new variants of the network summarisation problem. In edge-based weighted network summarisation, the summarised network should preserve edge weights as well as possible. A wider class of settings is considered in path-based weighted network summarisation, where the resulting summarised network should preserve longer range connectivities between nodes. Node-based weighted network summarisation in turn allows weights also on nodes and summarisation aims to preserve more information related to high weight nodes. We study theoretical properties of these problems and show them to be NP-hard. We propose a range of heuristic generalisation algorithms with different trade-offs between complexity and quality of the result. Comprehensive experiments on real data show that weighted networks can be summarised efficiently with relatively little error.Peer reviewe

Hybrid Keyword Extraction Algorithm and Cosine Similarity for Improving Sentences Cohesion in Text Summarization

As the amount of online information increases, systems that can automatically summarize text in a document become increasingly desirable. The main goal of a text summarization is to present the main ideas in a document in less space. In the create text summarization, there are two procedures which are extraction and abstraction procedure. One of extraction procedure is using keyword extraction algorithm which is easier and common but has problems in the lack of cohesion or correlation between sentences. The cohesion between sentences can be applied by using a cosine similarity method. In this study, a hybrid keyword extraction algorithm and cosine similarity for improving sentences cohesion in text summarization has been proposed. The proposed method using compression various compression ratios is used to create candidate of the summary. The result show that proposed method could affect significant increasing cohesion degree after evaluated in the t-Test. The result also shows that 50% compression ratio obtains the best result with Recall, Precision, and F-Measure are 0.761, 0.43 and 0.54 respectively; since summary with compression ratio 50% has higher intersection with human summary than another compression ratio

Automatic text summarization using pathfinder network scaling

Contém uma errataTese de Mestrado. Inteligência Artificial e Sistemas Inteligentes. Faculdade de Engenharia. Universidade do Porto, Faculdade de Economia. Universidade do Porto. 200