2 research outputs found
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking
This paper focuses on the end-to-end abstractive summarization of a single
product review without supervision. We assume that a review can be described as
a discourse tree, in which the summary is the root, and the child sentences
explain their parent in detail. By recursively estimating a parent from its
children, our model learns the latent discourse tree without an external parser
and generates a concise summary. We also introduce an architecture that ranks
the importance of each sentence on the tree to support summary generation
focusing on the main review point. The experimental results demonstrate that
our model is competitive with or outperforms other unsupervised approaches. In
particular, for relatively long reviews, it achieves a competitive or better
performance than supervised models. The induced tree shows that the child
sentences provide additional information about their parent, and the generated
summary abstracts the entire review.Comment: 13 pages, ACL 2019 (long paper
Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study
Automatic process discovery from textual process documentations is highly
desirable to reduce time and cost of Business Process Management (BPM)
implementation in organizations. However, existing automatic process discovery
approaches mainly focus on identifying activities out of the documentations.
Deriving the structural relationships between activities, which is important in
the whole process discovery scope, is still a challenge. In fact, a business
process has latent semantic hierarchical structure which defines different
levels of detail to reflect the complex business logic. Recent findings in
neural machine learning area show that the meaningful linguistic structure can
be induced by joint language modeling and structure learning. Inspired by these
findings, we propose to retrieve the latent hierarchical structure present in
the textual business process documents by building a neural network that
leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with
process-level language model objective. We tested the proposed approach on data
set of Process Description Documents (PDD) from our practical Robotic Process
Automation (RPA) projects. Preliminary experiments showed promising results