403 research outputs found
Abstractive Multi-Document Summarization via Phrase Selection and Merging
We propose an abstraction-based multi-document summarization framework that
can construct new sentences by exploring more fine-grained syntactic units than
sentences, namely, noun/verb phrases. Different from existing abstraction-based
approaches, our method first constructs a pool of concepts and facts
represented by phrases from the input documents. Then new sentences are
generated by selecting and merging informative phrases to maximize the salience
of phrases and meanwhile satisfy the sentence construction constraints. We
employ integer linear optimization for conducting phrase selection and merging
simultaneously in order to achieve the global optimal solution for a summary.
Experimental results on the benchmark data set TAC 2011 show that our framework
outperforms the state-of-the-art models under automated pyramid evaluation
metric, and achieves reasonably well results on manual linguistic quality
evaluation.Comment: 11 pages, 1 figure, accepted as a full paper at ACL 201
TimeMachine: Timeline Generation for Knowledge-Base Entities
We present a method called TIMEMACHINE to generate a timeline of events and
relations for entities in a knowledge base. For example for an actor, such a
timeline should show the most important professional and personal milestones
and relationships such as works, awards, collaborations, and family
relationships. We develop three orthogonal timeline quality criteria that an
ideal timeline should satisfy: (1) it shows events that are relevant to the
entity; (2) it shows events that are temporally diverse, so they distribute
along the time axis, avoiding visual crowding and allowing for easy user
interaction, such as zooming in and out; and (3) it shows events that are
content diverse, so they contain many different types of events (e.g., for an
actor, it should show movies and marriages and awards, not just movies). We
present an algorithm to generate such timelines for a given time period and
screen size, based on submodular optimization and web-co-occurrence statistics
with provable performance guarantees. A series of user studies using Mechanical
Turk shows that all three quality criteria are crucial to produce quality
timelines and that our algorithm significantly outperforms various baseline and
state-of-the-art methods.Comment: To appear at ACM SIGKDD KDD'15. 12pp, 7 fig. With appendix. Demo and
other info available at http://cs.stanford.edu/~althoff/timemachine
Multi-Document Summarization via Discriminative Summary Reranking
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 summaries for different
document sets, and even a summarization model with good overall performance may
produce low-quality summaries for some document sets. On the contrary, a
baseline summarization model may produce high-quality summaries for some
document sets. Based on the above observations, we treat the summaries produced
by different summarization models as candidate summaries, and then explore
discriminative reranking techniques to identify high-quality summaries from the
candidates for difference document sets. We propose to extract a set of
candidate summaries for each document set based on an ILP framework, and then
leverage Ranking SVM for summary reranking. Various useful features have been
developed for the reranking process, including word-level features,
sentence-level features and summary-level features. Evaluation results on the
benchmark DUC datasets validate the efficacy and robustness of our proposed
approach
Learning to Extract Coherent Summary via Deep Reinforcement Learning
Coherence plays a critical role in producing a high-quality summary from a
document. In recent years, neural extractive summarization is becoming
increasingly attractive. However, most of them ignore the coherence of
summaries when extracting sentences. As an effort towards extracting coherent
summaries, we propose a neural coherence model to capture the cross-sentence
semantic and syntactic coherence patterns. The proposed neural coherence model
obviates the need for feature engineering and can be trained in an end-to-end
fashion using unlabeled data. Empirical results show that the proposed neural
coherence model can efficiently capture the cross-sentence coherence patterns.
Using the combined output of the neural coherence model and ROUGE package as
the reward, we design a reinforcement learning method to train a proposed
neural extractive summarizer which is named Reinforced Neural Extractive
Summarization (RNES) model. The RNES model learns to optimize coherence and
informative importance of the summary simultaneously. Experimental results show
that the proposed RNES outperforms existing baselines and achieves
state-of-the-art performance in term of ROUGE on CNN/Daily Mail dataset. The
qualitative evaluation indicates that summaries produced by RNES are more
coherent and readable.Comment: 8 pages, 1 figure, presented at AAAI-201
A Multilingual Study of Compressive Cross-Language Text Summarization
Cross-Language Text Summarization (CLTS) generates summaries in a language
different from the language of the source documents. Recent methods use
information from both languages to generate summaries with the most informative
sentences. However, these methods have performance that can vary according to
languages, which can reduce the quality of summaries. In this paper, we propose
a compressive framework to generate cross-language summaries. In order to
analyze performance and especially stability, we tested our system and
extractive baselines on a dataset available in four languages (English, French,
Portuguese, and Spanish) to generate English and French summaries. An automatic
evaluation showed that our method outperformed extractive state-of-art CLTS
methods with better and more stable ROUGE scores for all languages
Creating Capsule Wardrobes from Fashion Images
We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfit combinations, we develop submodular objective functions capturing
the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible
outfits, we devise an iterative approach to allow near-optimal submodular
function maximization. Finally, we present an unsupervised approach to learn
visual compatibility from "in the wild" full body outfit photos; the
compatibility metric translates well to cleaner catalog photos and improves
over existing methods. Our results on thousands of pieces from popular fashion
websites show that automatic capsule creation has potential to mimic skilled
fashionistas in assembling flexible wardrobes, while being significantly more
scalable.Comment: Accepted to CVPR 201
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