  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.
