265 research outputs found
Contrastive Hierarchical Discourse Graph for Scientific Document Summarization
The extended structural context has made scientific paper summarization a
challenging task. This paper proposes CHANGES, a contrastive hierarchical graph
neural network for extractive scientific paper summarization. CHANGES
represents a scientific paper with a hierarchical discourse graph and learns
effective sentence representations with dedicated designed hierarchical graph
information aggregation. We also propose a graph contrastive learning module to
learn global theme-aware sentence representations. Extensive experiments on the
PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the
importance of capturing hierarchical structure information in modeling
scientific papers.Comment: CODI at ACL 202
HEGEL: Hypergraph Transformer for Long Document Summarization
Extractive summarization for long documents is challenging due to the
extended structured input context. The long-distance sentence dependency
hinders cross-sentence relations modeling, the critical step of extractive
summarization. This paper proposes HEGEL, a hypergraph neural network for long
document summarization by capturing high-order cross-sentence relations. HEGEL
updates and learns effective sentence representations with hypergraph
transformer layers and fuses different types of sentence dependencies,
including latent topics, keywords coreference, and section structure. We
validate HEGEL by conducting extensive experiments on two benchmark datasets,
and experimental results demonstrate the effectiveness and efficiency of HEGEL.Comment: EMNLP 202
SummIt: Iterative Text Summarization via ChatGPT
Existing text summarization systems have made significant progress in recent
years but typically generates summaries in a single step. The one-shot
summarization setting is sometimes inadequate, however, as the generated
summary may contain hallucinations or overlook important details related to the
reader's interests. In this paper, we address this limitation by proposing
SummIt, an iterative text summarization framework based on large language
models like ChatGPT. Our framework enables the model to refine the generated
summary iteratively through self-evaluation and feedback, closely resembling
the iterative process humans undertake when drafting and revising summaries. We
also explore using in-context learning to guide the rationale generation and
summary refinement. Furthermore, we explore the potential benefits of
integrating knowledge and topic extractors into the framework to enhance
summary faithfulness and controllability. We evaluate the performance of our
framework on three benchmark summarization datasets through empirical and
qualitative analyses. We also conduct a human evaluation to validate the
effectiveness of the model's refinements and find a potential issue of
over-correction. Our code is available at
\url{https://github.com/hpzhang94/summ_it}.Comment: work in progres
Extractive Summarization via ChatGPT for Faithful Summary Generation
Extractive summarization is a crucial task in natural language processing
that aims to condense long documents into shorter versions by directly
extracting sentences. The recent introduction of ChatGPT has attracted
significant interest in the NLP community due to its remarkable performance on
a wide range of downstream tasks. However, concerns regarding factuality and
faithfulness have hindered its practical applications for summarization
systems. This paper first presents a thorough evaluation of ChatGPT's
performance on extractive summarization and compares it with traditional
fine-tuning methods on various benchmark datasets. Our experimental analysis
reveals that ChatGPT's extractive summarization performance is still inferior
to existing supervised systems in terms of ROUGE scores. In addition, we
explore the effectiveness of in-context learning and chain-of-thought reasoning
for enhancing its performance. Furthermore, we find that applying an
extract-then-generate pipeline with ChatGPT yields significant performance
improvements over abstractive baselines in terms of summary faithfulness. These
observations highlight potential directions for enhancing ChatGPT's
capabilities for faithful text summarization tasks using two-stage approaches.Comment: Work in progres
Evidence for Co-rotation Origin of Super Metal Rich Stars in LAMOST-Gaia: Multiple Ridges with a Similar Slope in phi versus Lz Plane
Super metal-rich (SMR) stars in the solar neighborhood are thought to be born
in the inner disk and came to present location by radial migration, which is
most intense at the co-rotation resonance (CR) of the Galactic bar. In this
work, we show evidence for the CR origin of SMR stars in LAMOST-Gaia by
detecting six ridges and undulations in the phi versus Lz space coded by median
VR, following a similar slope of -8 km/s kpc/deg. The slope is predicted by
Monario et al.'s model for CR of a large and slow Galactic bar. For the first
time, we show the variation of angular momentum with azimuths from -10 deg to
20 deg for two outer and broad undulations with negative VR around -18 km/s
following this slope. The wave-like pattern with large amplitude outside CR and
a wide peak of the second undulations indicate that minor merger of the
Sagittarius dwarf galaxy with the disk might play a role besides the
significant impact of CR of the Galactic bar.Comment: 5 pages, 3 figures, published in ApJ Lette
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