## 6 Survey Findings and Future Directions
### Future Directions  
The findings of this bibliometric analysis reveal several promising avenues for future research in the field of text summarisation. Based on the identified trends, challenges, and gaps, the following directions are proposed  
* **Improved Abstractive Summarisation--**Develop more coherent and semantically accurate abstractive models [55] that generate summaries closer to human-written content. Focus on reducing issues such as factual inconsistencies and hallucinations in summaries by integrating external knowledge bases. * **Domain-Specific Summarisation--**Create customised summarisation systems for specialised fields such as healthcare, law, and finance. Emphasize the development of domain-specific datasets and pre-trained models tailored to industry jargon and technical content [56]. * **Real-Time Summarisation--**Design efficient models capable of summarising streaming data from sources [57] like social media, live news feeds, and real-time analytics. Incorporate temporal dynamics and event detection to enhance the relevance and timeliness of the generated summaries. * **Multimodal Summarisation--**Focus on building models that integrate audio, video, images, multiple languages [17], and other types of data. Develop systems that extract and generate important information from different forms of data. * **Dialogue Summarisation--**Improve summarisation techniques for multi-turn conversations [58], such as those in meetings, interviews, or customer service interactions. Address challenges like preserving speaker-specific information, maintaining coherence, and summarising complex dialogue structures. * **Bias and Ethics--**Mitigate biases [59] inherent in training datasets that can lead to skewed or unfair summaries. Develop ethical guidelines and frameworks for creating transparent, fair, and accountable summarisation systems. Introduce tools to detect and rectify biased summaries in real time. * **Multilingual Summarisation--**Extend summarisation capabilities to support multiple languages [17], especially those with limited resources. Leverage transfer learning and multilingual transformer models to build systems adaptable to diverse linguistic contexts. * **Interactive Summarisation--**Develop interactive systems that allow users to customize summaries by specifying preferences, such as length, focus, or tone [60]. Incorporate feedback mechanisms to improve summary quality based on user interactions. * **Low-Resource Summarisation--**Address the lack of large datasets for low-resource languages or domains by exploring data augmentation techniques and unsupervised learning [61]. Utilize transfer learning and cross-lingual approaches to adapt high-resource models for low-resource settings. * **Explainability--**Enhance model interpretability [62] by providing insights into why specific sentences or phrases were selected for the summary. Develop visualisation tools that highlight attention weights and decision pathways in the summarisation process. * **Long Document Summarisation--**Design models optimised for processing and summarising lengthy documents, such as books, legal texts, and research articles. Focus on improving memory efficiency [63] and coherence over extended content using techniques like hierarchical attention. * **Better Evaluation Metrics--**Move beyond traditional metrics like ROUGE and BLEU to develop comprehensive, human-centric evaluation frameworks. Incorporate measures of semantic accuracy, readability, coherence, and task-specific performance for a holistic assessment [64]. Text summarisation research has significant implications for information processing, but ethical concerns need to be addressed as technology evolves. Key issues include ensuring accuracy to avoid misrepresentation, mitigating bias to ensure fairness, protecting privacy through anonymisation, and obtaining consent for data usage. Transparency in methods and accountability for model outputs are essential to build trust.