3 research outputs found
Exclusive Hierarchical Decoding for Deep Keyphrase Generation
Keyphrase generation (KG) aims to summarize the main ideas of a document into
a set of keyphrases. A new setting is recently introduced into this problem, in
which, given a document, the model needs to predict a set of keyphrases and
simultaneously determine the appropriate number of keyphrases to produce.
Previous work in this setting employs a sequential decoding process to generate
keyphrases. However, such a decoding method ignores the intrinsic hierarchical
compositionality existing in the keyphrase set of a document. Moreover,
previous work tends to generate duplicated keyphrases, which wastes time and
computing resources. To overcome these limitations, we propose an exclusive
hierarchical decoding framework that includes a hierarchical decoding process
and either a soft or a hard exclusion mechanism. The hierarchical decoding
process is to explicitly model the hierarchical compositionality of a keyphrase
set. Both the soft and the hard exclusion mechanisms keep track of
previously-predicted keyphrases within a window size to enhance the diversity
of the generated keyphrases. Extensive experiments on multiple KG benchmark
datasets demonstrate the effectiveness of our method to generate less
duplicated and more accurate keyphrases.Comment: ACL 202
An Empirical Study on Neural Keyphrase Generation
Recent years have seen a flourishing of neural keyphrase generation works,
including the release of several large-scale datasets and a host of new models
to tackle them. Model performance on keyphrase generation tasks has increased
significantly with evolving deep learning research. However, there lacks a
comprehensive comparison among models, and an investigation on related factors
(e.g., architectural choice, decoding strategy) that may affect a keyphrase
generation system's performance. In this empirical study, we aim to fill this
gap by providing extensive experimental results and analyzing the most crucial
factors impacting the performance of keyphrase generation models. We hope this
study can help clarify some of the uncertainties surrounding the keyphrase
generation task and facilitate future research on this topic
A Condense-then-Select Strategy for Text Summarization
Select-then-compress is a popular hybrid, framework for text summarization
due to its high efficiency. This framework first selects salient sentences and
then independently condenses each of the selected sentences into a concise
version. However, compressing sentences separately ignores the context
information of the document, and is therefore prone to delete salient
information. To address this limitation, we propose a novel
condense-then-select framework for text summarization. Our framework first
concurrently condenses each document sentence. Original document sentences and
their compressed versions then become the candidates for extraction. Finally,
an extractor utilizes the context information of the document to select
candidates and assembles them into a summary. If salient information is deleted
during condensing, the extractor can select an original sentence to retain the
information. Thus, our framework helps to avoid the loss of salient
information, while preserving the high efficiency of sentence-level
compression. Experiment results on the CNN/DailyMail, DUC-2002, and Pubmed
datasets demonstrate that our framework outperforms the select-then-compress
framework and other strong baselines.Comment: Accepted by Knowledge-Based Systems (KBS) journa