Automatic text summarization (TS) plays a pivotal role in condensing large
volumes of information into concise, coherent summaries, facilitating efficient
information retrieval and comprehension. This paper presents a novel framework
for abstractive TS of single documents, which integrates three dominant
aspects: structural, semantic, and neural-based approaches. The proposed
framework merges machine learning and knowledge-based techniques to achieve a
unified methodology. The framework consists of three main phases:
pre-processing, machine learning, and post-processing. In the pre-processing
phase, a knowledge-based Word Sense Disambiguation (WSD) technique is employed
to generalize ambiguous words, enhancing content generalization. Semantic
content generalization is then performed to address out-of-vocabulary (OOV) or
rare words, ensuring comprehensive coverage of the input document.
Subsequently, the generalized text is transformed into a continuous vector
space using neural language processing techniques. A deep sequence-to-sequence
(seq2seq) model with an attention mechanism is employed to predict a
generalized summary based on the vector representation. In the post-processing
phase, heuristic algorithms and text similarity metrics are utilized to refine
the generated summary further. Concepts from the generalized summary are
matched with specific entities, enhancing coherence and readability.
Experimental evaluations conducted on prominent datasets, including Gigaword,
Duc 2004, and CNN/DailyMail, demonstrate the effectiveness of the proposed
framework. Results indicate significant improvements in handling rare and OOV
words, outperforming existing state-of-the-art deep learning techniques. The
proposed framework presents a comprehensive and unified approach towards
abstractive TS, combining the strengths of structure, semantics, and
neural-based methodologies.

该研究提出了一个新颖的抽象式单文档文本摘要框架，融合了结构、语义和基于神经网络的方法，通过结合机器学习和基于知识的技术实现了一种统一的方法。实验证明了该框架在处理罕见和未登录词方面的显著改进，优于现有的深度学习技术。

利用深度学习架构增强上下文理解的神经序列到序列模型与注意力的应用于抽象文本摘要

Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep  Learning Architectures for Enhanced Contextual Understanding in Abstractive  Text Summarization

Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.

此研究综述了分布式语义表示的理论背景，介绍了从单词级别向更精细的词义级别转化的方法，并提供了对两种主要分支进行的广泛技术概述；最终，对其重要方面进行了分析与评估。