1,713 research outputs found
Latent Network Summarization: Bridging Network Embedding and Summarization
Motivated by the computational and storage challenges that dense embeddings
pose, we introduce the problem of latent network summarization that aims to
learn a compact, latent representation of the graph structure with
dimensionality that is independent of the input graph size (i.e., #nodes and
#edges), while retaining the ability to derive node representations on the fly.
We propose Multi-LENS, an inductive multi-level latent network summarization
approach that leverages a set of relational operators and relational functions
(compositions of operators) to capture the structure of egonets and
higher-order subgraphs, respectively. The structure is stored in low-rank,
size-independent structural feature matrices, which along with the relational
functions comprise our latent network summary. Multi-LENS is general and
naturally supports both homogeneous and heterogeneous graphs with or without
directionality, weights, attributes or labels. Extensive experiments on real
graphs show 3.5 - 34.3% improvement in AUC for link prediction, while requiring
80 - 2152x less output storage space than baseline embedding methods on large
datasets. As application areas, we show the effectiveness of Multi-LENS in
detecting anomalies and events in the Enron email communication graph and
Twitter co-mention graph
D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling
Exponential growth of social media consumption demands effective user
interests-social contents modeling for more personalized recommendation and
social media summarization. However, due to the heterogeneous nature of social
contents, traditional approaches lack the ability of capturing the hidden
semantic correlations across these multi-modal data, which leads to semantic
gaps between social content understanding and user interests. To effectively
bridge the semantic gaps, we propose a novel deep learning framework for user
interests-social contents modeling. We first mine and parse data, i.e. textual
content, visual content, social context and social relation, from heterogeneous
social media feeds. Then, we design a two-branch network to map the social
contents and users into a same latent space. Particularly, the network is
trained by a large margin objective that combines a cross-instance distance
constraint with a within-instance semantic-preserving constraint in an end-to-
end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is
learned, and the ranking results can be given by calculating distances between
social contents and users. To demonstrate the effectiveness of D-Sempre in user
interests-social contents modeling, we construct a Twitter dataset and conduct
extensive experiments on it. As a result, D-Sempre effectively integrates the
multi-modal data from heterogeneous social media feeds and captures the hidden
semantic correlations between users' interests and social contents.Comment: ACM Multimedi
Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments
With the prevalence of video sharing, there are increasing demands for
automatic video digestion such as highlight detection. Recently, platforms with
crowdsourced time-sync video comments have emerged worldwide, providing a good
opportunity for highlight detection. However, this task is non-trivial: (1)
time-sync comments often lag behind their corresponding shot; (2) time-sync
comments are semantically sparse and noisy; (3) to determine which shots are
highlights is highly subjective. The present paper aims to tackle these
challenges by proposing a framework that (1) uses concept-mapped lexical-chains
for lag calibration; (2) models video highlights based on comment intensity and
combination of emotion and concept concentration of each shot; (3) summarize
each detected highlight using improved SumBasic with emotion and concept
mapping. Experiments on large real-world datasets show that our highlight
detection method and summarization method both outperform other benchmarks with
considerable margins.Comment: Accepted in EMNLP 2017 Workshop on New Frontiers in Summarization.
Please include "EMNLP 2017 Workshop on New Frontiers in Summarization" in any
citation
Semantic Sentence Embeddings for Paraphrasing and Text Summarization
This paper introduces a sentence to vector encoding framework suitable for
advanced natural language processing. Our latent representation is shown to
encode sentences with common semantic information with similar vector
representations. The vector representation is extracted from an encoder-decoder
model which is trained on sentence paraphrase pairs. We demonstrate the
application of the sentence representations for two different tasks -- sentence
paraphrasing and paragraph summarization, making it attractive for commonly
used recurrent frameworks that process text. Experimental results help gain
insight how vector representations are suitable for advanced language
embedding.Comment: 5 pages, 4 figures, IEEE GlobalSIP 2017 Conferenc
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
Identity stitching, the task of identifying and matching various online
references (e.g., sessions over different devices and timespans) to the same
user in real-world web services, is crucial for personalization and
recommendations. However, traditional user stitching approaches, such as
grouping or blocking, require quadratic pairwise comparisons between a massive
number of user activities, thus posing both computational and storage
challenges. Recent works, which are often application-specific, heuristically
seek to reduce the amount of comparisons, but they suffer from low precision
and recall. To solve the problem in an application-independent way, we take a
heterogeneous network-based approach in which users (nodes) interact with
content (e.g., sessions, websites), and may have attributes (e.g., location).
We propose node2bits, an efficient framework that represents multi-dimensional
features of node contexts with binary hashcodes. node2bits leverages
feature-based temporal walks to encapsulate short- and long-term interactions
between nodes in heterogeneous web networks, and adopts SimHash to obtain
compact, binary representations and avoid the quadratic complexity for
similarity search. Extensive experiments on large-scale real networks show that
node2bits outperforms traditional techniques and existing works that generate
real-valued embeddings by up to 5.16% in F1 score on user stitching, while
taking only up to 1.56% as much storage
Hierarchical Transformers for Multi-Document Summarization
In this paper, we develop a neural summarization model which can effectively
process multiple input documents and distill Transformer architecture with the
ability to encode documents in a hierarchical manner. We represent
cross-document relationships via an attention mechanism which allows to share
information as opposed to simply concatenating text spans and processing them
as a flat sequence. Our model learns latent dependencies among textual units,
but can also take advantage of explicit graph representations focusing on
similarity or discourse relations. Empirical results on the WikiSum dataset
demonstrate that the proposed architecture brings substantial improvements over
several strong baselines.Comment: to appear at ACL 201
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
In this work, we present a compact, modular framework for constructing novel
recurrent neural architectures. Our basic module is a new generic unit, the
Transition Based Recurrent Unit (TBRU). In addition to hidden layer
activations, TBRUs have discrete state dynamics that allow network connections
to be built dynamically as a function of intermediate activations. By
connecting multiple TBRUs, we can extend and combine commonly used
architectures such as sequence-to-sequence, attention mechanisms, and
re-cursive tree-structured models. A TBRU can also serve as both an encoder for
downstream tasks and as a decoder for its own task simultaneously, resulting in
more accurate multi-task learning. We call our approach Dynamic Recurrent
Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is
significantly more accurate and efficient than seq2seq with attention for
syntactic dependency parsing and yields more accurate multi-task learning for
extractive summarization tasks.Comment: 10 pages; Submitted for review to ACL201
SUMMARIZED: Efficient Framework for Analyzing Multidimensional Process Traces under Edit-distance Constraint
Domains such as scientific workflows and business processes exhibit data
models with complex relationships between objects. This relationship is
typically represented as sequences, where each data item is annotated with
multi-dimensional attributes. There is a need to analyze this data for
operational insights. For example, in business processes, users are interested
in clustering process traces into smaller subsets to discover less complex
process models. This requires expensive computation of similarity metrics
between sequence-based data. Related work on dimension reduction and embedding
methods do not take into account the multi-dimensional attributes of data, and
do not address the interpretability of data in the embedding space (i.e., by
favoring vector-based representation). In this work, we introduce Summarized, a
framework for efficient analysis on sequence-based multi-dimensional data using
intuitive and user-controlled summarizations. We introduce summarization
schemes that provide tunable trade-offs between the quality and efficiency of
analysis tasks and derive an error model for summary-based similarity under an
edit-distance constraint. Evaluations using real-world datasets show the
effectives of our framework
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
Inspired by how humans summarize long documents, we propose an accurate and
fast summarization model that first selects salient sentences and then rewrites
them abstractively (i.e., compresses and paraphrases) to generate a concise
overall summary. We use a novel sentence-level policy gradient method to bridge
the non-differentiable computation between these two neural networks in a
hierarchical way, while maintaining language fluency. Empirically, we achieve
the new state-of-the-art on all metrics (including human evaluation) on the
CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores.
Moreover, by first operating at the sentence-level and then the word-level, we
enable parallel decoding of our neural generative model that results in
substantially faster (10-20x) inference speed as well as 4x faster training
convergence than previous long-paragraph encoder-decoder models. We also
demonstrate the generalization of our model on the test-only DUC-2002 dataset,
where we achieve higher scores than a state-of-the-art model.Comment: ACL 2018 (17 pages
A Deep Reinforced Model for Abstractive Summarization
Attentional, RNN-based encoder-decoder models for abstractive summarization
have achieved good performance on short input and output sequences. For longer
documents and summaries however these models often include repetitive and
incoherent phrases. We introduce a neural network model with a novel
intra-attention that attends over the input and continuously generated output
separately, and a new training method that combines standard supervised word
prediction and reinforcement learning (RL). Models trained only with supervised
learning often exhibit "exposure bias" - they assume ground truth is provided
at each step during training. However, when standard word prediction is
combined with the global sequence prediction training of RL the resulting
summaries become more readable. We evaluate this model on the CNN/Daily Mail
and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the
CNN/Daily Mail dataset, an improvement over previous state-of-the-art models.
Human evaluation also shows that our model produces higher quality summaries
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