## 6 Survey Findings and Future Directions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author \& Year** & **Aim** & **Strategy** & **Key Feature** & **Limitation** \\ \hline [PERSON], [PERSON], [PERSON] [44], 2020 & Leveraging Pre-trained Checkpoints for Sequence Generation Tasks & Pre-trained models, fine-tuning, transfer learning & Adapt pre-trained models to specific tasks. Improves task performance & Pre-trained dependency, fine-tuning needs, domain mismatch, resource-intensive \\ \hline [PERSON], [PERSON], [PERSON] [45], 2020 & Longformer: The Long-Document Transformer & Efficient attention, long-document handling, transformer adaptation & Local attention, handles long documents, reduces memory requirements & Complex implementation, resource-intensive \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] [13], 2020 & PEGASUS: Pre-training with Extracted Gap-sentences & Gap-sentence generation, pre-training, fine-tuning summaries & Masks and predicts whole sentences, concise summaries & Data-heavy, computationally expensive \\ \hline [PERSON] et al. [46], 2020 & BART: Denoising Sequence-to-Sequence Pre-training & Denoising pre-training, encoder-decoder architecture & Corrupts and reconstructs, text for robustness, versatile applications & Computational cost, data requirements \\ \hline [PERSON], [PERSON] [47], 2019 & Text summarisation with Pretrained Encoders & Pretrained encoders, sequence-to-sequence model & Utilizes BERT for summarisation tasks, sequence-to-sequence approach & Computational cost, fine-tuning challenges \\ \hline [PERSON], [PERSON], [PERSON] [48], 2017 & Get to the Point: summarisation with Pointer-Generator Networks & Pointer-generator network, coverage mechanism & Combines generation and copying from original text & High computational requirements, repetition issues \\ \hline [PERSON], [PERSON], [PERSON] [9], 2015 & A Neural Attention Model for Abstractive summarisation & Neural network, attention mechanism, encoder-decoder framework & Focuses attention on key input parts for abstractive summarisation & Data dependence, complexity, resource-intensive \\ \hline [PERSON], [PERSON], [PERSON] [49], 2003 & Sentence Alignment for Monolingual Comparable Corpora & Sentence alignment, similarity measures, monolingual corpora & Aligns sentences based on similarity measures in related content & Scalability, alignment accuracy, complexity \\ \hline [PERSON], [PERSON], [PERSON], [PERSON] [50], 2002 & Machine Learning of Generic and User-Focused summarisation & Feature extraction, machine learning models, summarisation & Identifies relevant features for summarisation, machine learning-based & Data dependence, feature selection, scalability \\ \hline [PERSON] [51], 2004 & ROUGE: A Package for Automatic Evaluation of Summaries & Evaluation metrics, n-gram overlap & Precision, recall, F1 score for comparing summaries & Surface-level analysis, limited semantic understanding \\ \hline [PERSON], [PERSON], [PERSON] [52], 2005 & The Impact of Frequency on summarisation & Frequency analysis, sentence scoring, extraction & Ranks sentences by term frequency, compares frequency-based methods & Context ignorance, domain sensitivity \\ \hline [PERSON], [PERSON], [PERSON] [34], 2004 & LexRank: Graph-based Lexical Centrality & Graph-based ranking, LexRank algorithm, sentence selection & Uses a graph for sentence ranking based on lexical similarity & Computational complexity, domain sensitivity \\ \hline [PERSON], [PERSON], [PERSON] [6], 2004 & TextRank: Bringing Order into Texts & Graph-based ranking, TextRank algorithms & Uses PageRank to rank sentences in a graph based on similarity & Computationally intensive, context ignorance \\ \hline [PERSON], [PERSON], [PERSON] [3], 2001 & Generic summarisation Analysis & Relevance measure, latent semantic analysis & Uses LSA for dimensionality reduction, selects key sentences & Context ignorance, computational complexity \\ \hline [PERSON], [PERSON] [53], 2000 & Automated Acquisition of Topic Signatures & Topic signatures, sentence scoring, summary extraction & Acquires key terms for topics, ranks sentences based on them & Context ignorance, relevance overlap, complexity \\ \hline [PERSON] et al. [54], 1995 & A Trainable Document Summarizer & Trainable summarisation, feature extraction & Features include term frequency, sentence position, and cue phrases & Domain dependence, requires a large training corpus \\ \hline [PERSON] [11], 1969 & New Methods in Automatic Extracting & Statistical analysis, keyword weighting, human editing & Sentence position importance, keyword relevance & Limited scope, reliance on sentence position \\ \hline [PERSON] [10], 1958 & The Automatic Creation of Literature Abstracts & Word frequency, sentence scoring, selection criteria & Frequency analysis, content scoring, human editing & Simplistic scoring, manual intervention \\ \hline \hline \end{tabular}
\end{table}