We build a bridge between neural network-based machine learning and
graph-based natural language processing and introduce a unified approach to
keyphrase, summary and relation extraction by aggregating dependency graphs
from links provided by a deep-learning based dependency parser.
We reorganize dependency graphs to focus on the most relevant content
elements of a sentence, integrate sentence identifiers as graph nodes and after
ranking the graph, we extract our keyphrases and summaries from its largest
strongly-connected component. We take advantage of the implicit structural
information that dependency links bring to extract subject-verb-object, is-a
and part-of relations.
We put it all together into a proof-of-concept dialog engine that specializes
the text graph with respect to a query and reveals interactively the document's
most relevant content elements. The open-source code of the integrated system
is available at this https URL .
Keywords: graph-based natural language processing, dependency graphs,
keyphrase, summary and relation extraction, query-driven salient sentence
extraction, logic-based dialog engine, synergies between neural and symbolic
processing.

通过聚集深度学习方法提供的依存句法分析器提供的链接形成的依赖图来建立神经网络和基于图的自然语言处理之间的桥梁，并通过提取最大强连通组件中的关键词和摘要来介绍统一的关键词、摘要和关系提取方法，从而构建一个新的对话引擎，利用内在结构信息提取命题之间的关系。

依存关系文本图用于关键词和摘要提取，及其在交互式内容检索中的应用

Dependency-based Text Graphs for Keyphrase and Summary Extraction with  Applications to Interactive Content Retrieval

Large collections of videos are grouped into clusters by a topic keyword,
such as Eiffel Tower or Surfing, with many important visual concepts repeating
across them. Such a topically close set of videos have mutual influence on each
other, which could be used to summarize one of them by exploiting information
from others in the set. We build on this intuition to develop a novel approach
to extract a summary that simultaneously captures both important
particularities arising in the given video, as well as, generalities identified
from the set of videos. The topic-related videos provide visual context to
identify the important parts of the video being summarized. We achieve this by
developing a collaborative sparse optimization method which can be efficiently
solved by a half-quadratic minimization algorithm. Our work builds upon the
idea of collaborative techniques from information retrieval and natural
language processing, which typically use the attributes of other similar
objects to predict the attribute of a given object. Experiments on two
challenging and diverse datasets well demonstrate the efficacy of our approach
over state-of-the-art methods.

采用协作优化方法，利用视频聚类和主题相关视频提供的视觉背景，同时抓取视频的重要特点和归纳性信息，从而进行视频摘要提取，该方法在两个数据集上的实验证明了其优越性。

主题相关视频的协作摘要

Collaborative Summarization of Topic-Related Videos

Determinantal point processes (DPPs) are distributions over sets of items
that model diversity using kernels. Their applications in machine learning
include summary extraction and recommendation systems. Yet, the cost of
sampling from a DPP is prohibitive in large-scale applications, which has
triggered an effort towards efficient approximate samplers. We build a novel
MCMC sampler that combines ideas from combinatorial geometry, linear
programming, and Monte Carlo methods to sample from DPPs with a fixed sample
cardinality, also called projection DPPs. Our sampler leverages the ability of
the hit-and-run MCMC kernel to efficiently move across convex bodies. Previous
theoretical results yield a fast mixing time of our chain when targeting a
distribution that is close to a projection DPP, but not a DPP in general. Our
empirical results demonstrate that this extends to sampling projection DPPs,
i.e., our sampler is more sample-efficient than previous approaches which in
turn translates to faster convergence when dealing with costly-to-evaluate
functions, such as summary extraction in our experiments.

该研究构建了一种基于组合几何、线性规划和蒙特卡罗方法的新型 MCMC 抽样器，该抽样器利用 hit-and-run MCMC 核有效地跨越凸体并从 DPP 中抽样，提高了汇聚速度。