334 research outputs found
Title-Guided Encoding for Keyphrase Generation
Keyphrase generation (KG) aims to generate a set of keyphrases given a
document, which is a fundamental task in natural language processing (NLP).
Most previous methods solve this problem in an extractive manner, while
recently, several attempts are made under the generative setting using deep
neural networks. However, the state-of-the-art generative methods simply treat
the document title and the document main body equally, ignoring the leading
role of the title to the overall document. To solve this problem, we introduce
a new model called Title-Guided Network (TG-Net) for automatic keyphrase
generation task based on the encoder-decoder architecture with two new
features: (i) the title is additionally employed as a query-like input, and
(ii) a title-guided encoder gathers the relevant information from the title to
each word in the document. Experiments on a range of KG datasets demonstrate
that our model outperforms the state-of-the-art models with a large margin,
especially for documents with either very low or very high title length ratios.Comment: AAAI 1
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
Keyphrase Generation: A Multi-Aspect Survey
Extractive keyphrase generation research has been around since the nineties,
but the more advanced abstractive approach based on the encoder-decoder
framework and sequence-to-sequence learning has been explored only recently. In
fact, more than a dozen of abstractive methods have been proposed in the last
three years, producing meaningful keyphrases and achieving state-of-the-art
scores. In this survey, we examine various aspects of the extractive keyphrase
generation methods and focus mostly on the more recent abstractive methods that
are based on neural networks. We pay particular attention to the mechanisms
that have driven the perfection of the later. A huge collection of scientific
article metadata and the corresponding keyphrases is created and released for
the research community. We also present various keyphrase generation and text
summarization research patterns and trends of the last two decades.Comment: 10 pages, 5 tables. Published in proceedings of FRUCT 2019, the 25th
Conference of the Open Innovations Association FRUCT, Helsinki, Finlan
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