4 research outputs found
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles
Recent advances in natural language processing have enabled automation of a
wide range of tasks, including machine translation, named entity recognition,
and sentiment analysis. Automated summarization of documents, or groups of
documents, however, has remained elusive, with many efforts limited to
extraction of keywords, key phrases, or key sentences. Accurate abstractive
summarization has yet to be achieved due to the inherent difficulty of the
problem, and limited availability of training data. In this paper, we propose a
topic-centric unsupervised multi-document summarization framework to generate
extractive and abstractive summaries for groups of scientific articles across
20 Fields of Study (FoS) in Microsoft Academic Graph (MAG) and news articles
from DUC-2004 Task 2. The proposed algorithm generates an abstractive summary
by developing salient language unit selection and text generation techniques.
Our approach matches the state-of-the-art when evaluated on automated
extractive evaluation metrics and performs better for abstractive summarization
on five human evaluation metrics (entailment, coherence, conciseness,
readability, and grammar). We achieve a kappa score of 0.68 between two
co-author linguists who evaluated our results. We plan to publicly share
MAG-20, a human-validated gold standard dataset of topic-clustered research
articles and their summaries to promote research in abstractive summarization.Comment: 6 pages, 6 Figures, 8 Tables. Accepted at IEEE Big Data 2020
(https://bigdataieee.org/BigData2020/AcceptedPapers.html
Selecting Labels for News Document Clusters
Abstract. This work deals with determination of meaningful and terse cluster labels for News document clusters. We analyze a number of alternatives for selecting headlines and/or sentences of document in a document cluster (obtained as a result of an entity-event-duration query), and formalize an approach to extracting a short phrase from well-supported headlines/sentences of the cluster that can serve as the cluster label. Our technique maps a sentence into a set of significant stems to approximate its semantics, for comparison. Eventually a cluster label is extracted from a selected headline/sentence as a contiguous sequence of words, resuscitating word sequencing information lost in the formalization of semantic equivalence.
Selecting Labels for News Document Clusters
This work deals with determination of meaningful and terse cluster labels for News document clusters. We analyze a number of alternatives for selecting headlines and/or sentences of document in a document cluster (obtained as a result of an entity-event-duration query), and formalize an approach to extracting a short phrase from well-supported headlines/sentences of the cluster that can serve as the cluster label. Our technique maps a sentence into a set of significant stems to approximate its semantics, for comparison. Eventually a cluster label is extracted from a selected headline/sentence as a contiguous sequence of words, resuscitating word sequencing information lost in the formalization of semantic equivalence