554 research outputs found
Syntactically Look-Ahead Attention Network for Sentence Compression
Sentence compression is the task of compressing a long sentence into a short
one by deleting redundant words. In sequence-to-sequence (Seq2Seq) based
models, the decoder unidirectionally decides to retain or delete words. Thus,
it cannot usually explicitly capture the relationships between decoded words
and unseen words that will be decoded in the future time steps. Therefore, to
avoid generating ungrammatical sentences, the decoder sometimes drops important
words in compressing sentences. To solve this problem, we propose a novel
Seq2Seq model, syntactically look-ahead attention network (SLAHAN), that can
generate informative summaries by explicitly tracking both dependency parent
and child words during decoding and capturing important words that will be
decoded in the future. The results of the automatic evaluation on the Google
sentence compression dataset showed that SLAHAN achieved the best
kept-token-based-F1, ROUGE-1, ROUGE-2 and ROUGE-L scores of 85.5, 79.3, 71.3
and 79.1, respectively. SLAHAN also improved the summarization performance on
longer sentences. Furthermore, in the human evaluation, SLAHAN improved
informativeness without losing readability.Comment: AAAI 202
VoG: Summarizing and Understanding Large Graphs
How can we succinctly describe a million-node graph with a few simple
sentences? How can we measure the "importance" of a set of discovered subgraphs
in a large graph? These are exactly the problems we focus on. Our main ideas
are to construct a "vocabulary" of subgraph-types that often occur in real
graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the
most succinct description of a graph in terms of this vocabulary. We measure
success in a well-founded way by means of the Minimum Description Length (MDL)
principle: a subgraph is included in the summary if it decreases the total
description length of the graph.
Our contributions are three-fold: (a) formulation: we provide a principled
encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop
\method, an efficient method to minimize the description cost, and (c)
applicability: we report experimental results on multi-million-edge real
graphs, including Flickr and the Notre Dame web graph.Comment: SIAM International Conference on Data Mining (SDM) 201
{VoG}: {Summarizing} and Understanding Large Graphs
How can we succinctly describe a million-node graph with a few simple sentences? How can we measure the "importance" of a set of discovered subgraphs in a large graph? These are exactly the problems we focus on. Our main ideas are to construct a "vocabulary" of subgraph-types that often occur in real graphs (e.g., stars, cliques, chains), and from a set of subgraphs, find the most succinct description of a graph in terms of this vocabulary. We measure success in a well-founded way by means of the Minimum Description Length (MDL) principle: a subgraph is included in the summary if it decreases the total description length of the graph. Our contributions are three-fold: (a) formulation: we provide a principled encoding scheme to choose vocabulary subgraphs; (b) algorithm: we develop \method, an efficient method to minimize the description cost, and (c) applicability: we report experimental results on multi-million-edge real graphs, including Flickr and the Notre Dame web graph
The Minimum Description Length Principle for Pattern Mining: A Survey
This is about the Minimum Description Length (MDL) principle applied to
pattern mining. The length of this description is kept to the minimum.
Mining patterns is a core task in data analysis and, beyond issues of
efficient enumeration, the selection of patterns constitutes a major challenge.
The MDL principle, a model selection method grounded in information theory, has
been applied to pattern mining with the aim to obtain compact high-quality sets
of patterns. After giving an outline of relevant concepts from information
theory and coding, as well as of work on the theory behind the MDL and similar
principles, we review MDL-based methods for mining various types of data and
patterns. Finally, we open a discussion on some issues regarding these methods,
and highlight currently active related data analysis problems
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