## 6 Survey Findings and Future Directions
### Text Summarisation Frameworks  
This section presents a block diagram approach to illustrate the frameworks proposed in various research papers on text summarisation. The diagrams are discussed in detail and visually represented using block diagrams for a clearer understanding of the summarisation processes. In the figure, each model leverages distinct methodologies to process large pieces of text and generate concise summaries. The diagrams provide a visual representation of inputs, processes, and outputs for each model, facilitating a clearer understanding of their unique approaches. By delineating these methodologies, we aim to highlight the diversity of strategies available in text summarisation, providing insight into how each approach can be applied to enhance our data processing and summarisation objectives.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Figure and Model** & **Input** & **Key Methodology** & **Key Features** & **Summary Generation** \\ \hline
**Figure A1a** & & Frequency and position-based & Statistical methods, Term & Selects significant words and generates summary \\
**Figure A1b** & & & & \\ [PERSON] [11] & Large Text & Frequency, pragmatic words, and structural indicators & Hybrid approach (linguistic \& structural features) & Selects significant sentences \\ \hline
**Figure A1c** & & & & \\ [PERSON] et al. [54] & Large Text & Sentence scoring, Bayes’ & Supervised learning, Term & Chooses the most probable sentences based on Bayes’ probability \\ \hline
**Figure A1d** & & & & \\ [PERSON] and [PERSON] [53] & Large Text & Topic signature and sentence ranking & Semantic relevance, Corpus-based methods & Ranks sentences and selects the top-ranked ones \\ \hline
**Figure A1e** & & & & \\ [PERSON] and [PERSON] [3] & Matrix form & & & \\ \hline
**Figure A1f** & & & & \\ [PERSON] and [PERSON] [6] & Large Text & TextRank Graph & & \\  & & & & \\ \hline
**Figure A1g** & & & & \\ [PERSON] and [PERSON] [34] & Large Text & LexRank Graph & Lexical analysis, Sentence & Selects sentences from the graph \\ \hline
**Figure A1h** & & & & \\ [PERSON] et al. [52] & Large Text & Frequency-based method & Sentence probability revision & Selects highest-probability sentences iteratively \\ \hline
**Figure A1i** & & & & \\ [PERSON] and [PERSON] [50] & Large Text & SCDF learning algorithms & Adaptable learning rules & Customizes rules to generate summaries \\ \hline
**Figure A1j** & & & \\ [PERSON] and [PERSON] [49] & Large Text & Structural alignment of & Macro and Micro alignment & Aligns and generates summaries \\ \hline
**Figure A1k** & & & \\ [PERSON] et al. [9] & Large Text & Encoder-Decoder model & Attention mechanism, Beam search decoder & Generates summaries state-of-the-art results \\ \hline
**Figure A1l** & & & \\ [PERSON] et al. [48] & Large Text & LSTM Bi-directional Encoder & Attention mechanism or Unidirectional Decoder & Generates summaries \\ \hline
**Figure A1m** & & & \\ [PERSON] and [PERSON] [47] & Large Text & & \\  & & & \\ \hline
**Figure A1n** & & & \\ [PERSON] et al. [46] & Large Text & Random and pre-trained & Transfer learning for fine-tuning & Improves summarisation, machine translation, and Q&A tasks \\ \hline
**Figure A1o** & & & \\ [PERSON] et al. [13] & Large Text & Transformer encoder-decoder &
\begin{tabular}{l} Task-specific pre-training \\ objectives \\ \end{tabular} & Generates high-quality summaries \\ \hline
**Figure A1p** & & & & \\ [PERSON] et al. [45] & Large Text & Transformer Encoder-Decoder with Longformer & Local and global attention mechanisms & Reduces computational overhead for long-context tasks like document summarisation, improving efficiency and scalability. \\ \hline
**Figure A1q** & & & \\ [PERSON] et al. [44] & Large Text & Pre-trained & Transfer learning & Improves summarisation outputs \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of models and their methodologies for text summarisation.