13,702 research outputs found
Text Summarization Techniques: A Brief Survey
In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review the different processes for summarization and describe the effectiveness
and shortcomings of the different methods.Comment: Some of references format have update
Syntactic Topic Models
The syntactic topic model (STM) is a Bayesian nonparametric model of language
that discovers latent distributions of words (topics) that are both
semantically and syntactically coherent. The STM models dependency parsed
corpora where sentences are grouped into documents. It assumes that each word
is drawn from a latent topic chosen by combining document-level features and
the local syntactic context. Each document has a distribution over latent
topics, as in topic models, which provides the semantic consistency. Each
element in the dependency parse tree also has a distribution over the topics of
its children, as in latent-state syntax models, which provides the syntactic
consistency. These distributions are convolved so that the topic of each word
is likely under both its document and syntactic context. We derive a fast
posterior inference algorithm based on variational methods. We report
qualitative and quantitative studies on both synthetic data and hand-parsed
documents. We show that the STM is a more predictive model of language than
current models based only on syntax or only on topics
Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
To help their users to discover important items at a particular time, major
websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K
recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most
Viewed News Stories), which rely on crowdsourced popularity signals to select
the items. However, different sections of a crowd may have different
preferences, and there is a large silent majority who do not explicitly express
their opinion. Also, the crowd often consists of actors like bots, spammers, or
people running orchestrated campaigns. Recommendation algorithms today largely
do not consider such nuances, hence are vulnerable to strategic manipulation by
small but hyper-active user groups.
To fairly aggregate the preferences of all users while recommending top-K
items, we borrow ideas from prior research on social choice theory, and
identify a voting mechanism called Single Transferable Vote (STV) as having
many of the fairness properties we desire in top-K item (s)elections. We
develop an innovative mechanism to attribute preferences of silent majority
which also make STV completely operational. We show the generalizability of our
approach by implementing it on two different real-world datasets. Through
extensive experimentation and comparison with state-of-the-art techniques, we
show that our proposed approach provides maximum user satisfaction, and cuts
down drastically on items disliked by most but hyper-actively promoted by a few
users.Comment: In the proceedings of the Conference on Fairness, Accountability, and
Transparency (FAT* '19). Please cite the conference versio
Mapping Subsets of Scholarly Information
We illustrate the use of machine learning techniques to analyze, structure,
maintain, and evolve a large online corpus of academic literature. An emerging
field of research can be identified as part of an existing corpus, permitting
the implementation of a more coherent community structure for its
practitioners.Comment: 10 pages, 4 figures, presented at Arthur M. Sackler Colloquium on
"Mapping Knowledge Domains", 9--11 May 2003, Beckman Center, Irvine, CA,
proceedings to appear in PNA
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