Graph Neural Network and NER-Based Text Summarization
- URL: http://arxiv.org/abs/2402.05126v1
- Date: Mon, 5 Feb 2024 03:00:44 GMT
- Title: Graph Neural Network and NER-Based Text Summarization
- Authors: Imaad Zaffar Khan, Amaan Aijaz Sheikh, Utkarsh Sinha
- Abstract summary: This project introduces an innovative approach to text summarization, leveraging the capabilities of Graph Neural Networks (GNNs) and Named Entity Recognition (NER) systems.
Our method aims to enhance the efficiency of summarization and also tries to ensures a high degree relevance in the condensed content.
- Score: 1.5850926890180461
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the abundance of data and information in todays time, it is nearly
impossible for man, or, even machine, to go through all of the data line by
line. What one usually does is to try to skim through the lines and retain the
absolutely important information, that in a more formal term is called
summarization. Text summarization is an important task that aims to compress
lengthy documents or articles into shorter, coherent representations while
preserving the core information and meaning. This project introduces an
innovative approach to text summarization, leveraging the capabilities of Graph
Neural Networks (GNNs) and Named Entity Recognition (NER) systems. GNNs, with
their exceptional ability to capture and process the relational data inherent
in textual information, are adept at understanding the complex structures
within large documents. Meanwhile, NER systems contribute by identifying and
emphasizing key entities, ensuring that the summarization process maintains a
focus on the most critical aspects of the text. By integrating these two
technologies, our method aims to enhances the efficiency of summarization and
also tries to ensures a high degree relevance in the condensed content. This
project, therefore, offers a promising direction for handling the ever
increasing volume of textual data in an information-saturated world.
Related papers
- Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data.
We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation.
Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
arXiv Detail & Related papers (2024-10-31T06:55:24Z) - See then Tell: Enhancing Key Information Extraction with Vision Grounding [54.061203106565706]
We introduce STNet (See then Tell Net), a novel end-to-end model designed to deliver precise answers with relevant vision grounding.
To enhance the model's seeing capabilities, we collect extensive structured table recognition datasets.
arXiv Detail & Related papers (2024-09-29T06:21:05Z) - Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information.
Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
arXiv Detail & Related papers (2024-06-18T13:35:25Z) - Label-Free Topic-Focused Summarization Using Query Augmentation [2.127049691404299]
This study introduces a novel method, Augmented-Query Summarization (AQS), for topic-focused summarization without the need for extensive labelled datasets.
Our method demonstrates the ability to generate relevant and accurate summaries, showing its potential as a cost-effective solution in data-rich environments.
This innovation paves the way for broader application and accessibility in the field of topic-focused summarization technology.
arXiv Detail & Related papers (2024-04-25T08:39:10Z) - Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization [0.0]
This paper presents a novel framework for abstractive TS of single documents.
It integrates three dominant aspects: structure, semantic, and neural-based approaches.
Results indicate significant improvements in handling rare and OOV words.
arXiv Detail & Related papers (2024-04-08T18:33:59Z) - Factually Consistent Summarization via Reinforcement Learning with
Textual Entailment Feedback [57.816210168909286]
We leverage recent progress on textual entailment models to address this problem for abstractive summarization systems.
We use reinforcement learning with reference-free, textual entailment rewards to optimize for factual consistency.
Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience, and conciseness of the generated summaries.
arXiv Detail & Related papers (2023-05-31T21:04:04Z) - CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation [25.56539617837482]
A novel context-aware graph-attention model (Context-aware GAT) is proposed.
It assimilates global features from relevant knowledge graphs through a context-enhanced knowledge aggregation mechanism.
Empirical results demonstrate that our framework outperforms conventional GNN-based language models in terms of performance.
arXiv Detail & Related papers (2023-05-10T16:31:35Z) - TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
We propose a novel text-rich graph neural network with external knowledge (TeKo)
We first present a flexible heterogeneous semantic network that incorporates high-quality entities.
We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
arXiv Detail & Related papers (2022-06-15T02:33:10Z) - Knowledge Graph Augmented Network Towards Multiview Representation
Learning for Aspect-based Sentiment Analysis [96.53859361560505]
We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information.
KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based.
Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
arXiv Detail & Related papers (2022-01-13T08:25:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.