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Register a new userTimeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport
تلخيص الجدول الزمني على أساس ضغط الرسم البياني للحدث عبر النقل الأمثل
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يحدد تلخيص الجدول الزمني الأحداث الرئيسية من مجموعة أخبار ويصفها بعد النظام الزمني، مع التواريخ الرئيسية الموسومة. الأساليب السابقة تولد عموما ملخصات بشكل منفصل لكل تاريخ بعد تحديد تواريخ الأحداث الرئيسية. تطل هذه الطرق على الأحداث "Intra-Interra -ysures (الحجج) والهياكل المختلفة (اتصالات أحداث الأحداث). بعد مسار مختلف، نقترح تمثيل المقالات الإخبارية كشركة بيانية حدث، وبالتالي تصبح التلخيص ضغط الرسم البياني بأكمله إلى الرسم البياني الفرعي البارز. الفرضية الرئيسية هي أن الأحداث المتصلة بها من خلال الوسائط المشتركة والنظام الزمني تصور هيكل عظمي جدول زمني، يحتوي على أحداث ذات صلة من الناحية الدلوية، متماسكة مؤقتا ومربحا هيكليا في الرسم البياني العالمي للحدث. يتم بعد ذلك إدخال مسافة النقل الأمثل التي تدركها على تعلم نموذج الضغط بطريقة غير منشأة. نظرا لأن نهجنا يتحسن بشكل كبير على حالة الفن على ثلاث مجموعات بيانات حقيقية، بما في ذلك معايير قياسية عامة ومجموعة بيانات الطوارئ 10 المجمعة حديثا.
Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged. Previous methods generally generate summaries separately for each date after they determine the key dates of events. These methods overlook the events' intra-structures (arguments) and inter-structures (event-event connections). Following a different route, we propose to represent the news articles as an event-graph, thus the summarization becomes compressing the whole graph to its salient sub-graph. The key hypothesis is that the events connected through shared arguments and temporal order depict the skeleton of a timeline, containing events that are semantically related, temporally coherent and structurally salient in the global event graph. A time-aware optimal transport distance is then introduced for learning the compression model in an unsupervised manner. We show that our approach significantly improves on the state of the art on three real-world datasets, including two public standard benchmarks and our newly collected Timeline100 dataset.
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Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events
in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
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Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting both the intra and cross-document rel
ations between sentences; (2) neglecting the coherence and conciseness of the whole summary. In this paper, we propose a novel MDS framework (SgSum) to formulate the MDS task as a sub-graph selection problem, in which source documents are regarded as a relation graph of sentences (e.g., similarity graph or discourse graph) and the candidate summaries are its sub-graphs. Instead of selecting salient sentences, SgSum selects a salient sub-graph from the relation graph as the summary. Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent. Extensive experiments on MultiNews and DUC datasets show that our proposed method brings substantial improvements over several strong baselines. Human evaluation results also demonstrate that our model can produce significantly more coherent and informative summaries compared with traditional MDS methods. Moreover, the proposed architecture has strong transfer ability from single to multi-document input, which can reduce the resource bottleneck in MDS tasks.
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detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces.
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