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In this paper, we propose the concept of summary prior to define how much a sentence is appropriate to be selected into summary without consideration of its context. Different from previous work using manually compiled document-independent features, we develop a novel summary system called PriorSum, which applies the enhanced convolutional neu-ral networks(More)
We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document sum-marization. It formulates the sentence ranking task as a hierarchical regression process, which simultaneously measures the salience of a sentence and its constituents (e.g., phrases) in the parsing tree. This enables us to draw on word-level to(More)
This document demonstrates our participant system PolyU on CL-SciSumm 2016. There are three tasks in CL-SciSumm 2016. In Task 1A, we apply SVM Rank to identify the spans of text in the reference paper reflecting the citance. In Task 1B, we use the decision tree to classify the facet that a ci-tance belongs to. Finally, in Task 2, we develop an enhanced(More)
Topic modeling techniques have the benefits of model-ing words and documents uniformly under a probabilis-tic framework. However, they also suffer from the limitations of sensitivity to initialization and unigram topic distribution, which can be remedied by deep learning techniques. To explore the combination of topic mod-eling and deep learning techniques,(More)
The development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social me-dia's reactions. We utilize two types of social labels in tweets, i.e., hashtags and(More)
The paper presents a scenario-driven multi-agent framework for rapid simulation generation and verification. Specifically, this framework has the following features: 1. Formal scenario model: This framework uses a formalized scenario model, and in this model each scenario is specified according to the ACDATE (Actors, Conditions, Data, Actions, Timing, and(More)
Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the two tasks in isolation. However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained(More)
Existing multi-document summarization systems usually rely on a specific summarization model (i.e., a summarization method with a specific parameter setting) to extract summaries for different document sets with different topics. However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality(More)