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

arXiv: https://arxiv.org/abs/2404.08685 · HTML: https://mlsanity.com/paper/2404.08685/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders](https://mlsanity.com/paper/2005.02590/) (2020)
- [Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings](https://mlsanity.com/paper/1909.10430/) (2019)
- [EmbraceNet: A robust deep learning architecture for multimodal classification](https://mlsanity.com/paper/1904.09078/) (2019)
- [NCRF++: An Open-source Neural Sequence Labeling Toolkit](https://mlsanity.com/paper/1806.05626/) (2018)
- [sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings](https://mlsanity.com/paper/1511.06388/) (2015)
- [Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation](https://mlsanity.com/paper/1905.05677/) (2019)
- [SemEval-2017 Task 3: Community Question Answering](https://mlsanity.com/paper/1912.00730/) (2019)
- [Attending to Characters in Neural Sequence Labeling Models](https://mlsanity.com/paper/1611.04361/) (2016)

## Built on by (most cited first, 4 of 4)

- [A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks](https://mlsanity.com/paper/2502.16484/) (2025)
- [Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction](https://mlsanity.com/paper/2404.15347/) (2024)
- [Generative Fuzzy System for Sequence Generation](https://mlsanity.com/paper/2411.13867/) (2024)
- [Interpreting Public Sentiment in Diplomacy Events: A Counterfactual Analysis Framework Using Large Language Models](https://mlsanity.com/paper/2509.20367/) (2025)

## Abstract (arXiv)

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.