# Automatic Short Text Summarization Techniques in Social Media Platforms
## 8 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Objective** & **Method** & **Domain** & **Dataset** &
\begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} & **Observation** \\ \hline
[41] & Salience estimation & \begin{tabular}{c} Semantic types \\ and graph \\ convolutional \\ network \\ \end{tabular} & Generic & \begin{tabular}{c} Collected tweets for \\ 1000 events \\ \end{tabular} & \
\begin{tabular}{c} ROUGE-2 = 0.647 \\ BLEU = 0.490 \\ \end{tabular} &
\begin{tabular}{c} Two extractive strategies were \\ investigated. The first ranks \\ tweets \\ second generates; the \\ \end{tabular} \\ \hline
[56] & \begin{tabular}{c} Redundancy and \\ accuracy \\ \end{tabular} & Graph-based & Specific events & \begin{tabular}{c} Iran Election, \\ IranNuclearProgram, \\ and USPresidentialDebates \\ \end{tabular} & \
\begin{tabular}{c} ROUGE-1 = 0.6239 \\ ROUGE-2 = 0.5476 \\ \end{tabular} &
\begin{tabular}{c} This method identifies the \\ significant events, summarizes \\ them, and generates a coherent \\ storyline of their evolution with \\ a reasonable computational cost \\ for large datasets. \\ \end{tabular} \\ \hline
[61] & Quality & \begin{tabular}{c} Multi- \\ objective \\ DE \\ \end{tabular} & \begin{tabular}{c} Specific \\ Topic \\ \end{tabular} & \begin{tabular}{c} Dataset collected \\ from NLP6 CC \\ \end{tabular} & \
\begin{tabular}{c} ROUGE-1 = 0.531706 \\ ROUGE-2 = 0.26579 \\ ROUGE-SU = 0.266479 \\ \end{tabular} &
\begin{tabular}{c} This method suggests a \\ summary technique that takes \\ the Paragraph Vector \\ semantic structure into account. \\ \end{tabular} \\ \hline
[45] & \begin{tabular}{c} Detection, Fast \\ Happening \\ Subevents, and \\ hierarchy \\ \end{tabular} & LSTM & Specific events & \begin{tabular}{c} FA CUP final, ST, \\ Primaries, and USEs \\ SB (entry, ofwww. \\ socialapse,e/ \\ results/datasets/7.2 \\ + twitter-16.4 \\ \end{tabular} & \
\begin{tabular}{c} This method generates a storyline \\ for an event using summaries at \\ different levels, but it needs \\ separate deep learning module \\ on the USE dataset \\ August 2023 \\ \end{tabular} &
\begin{tabular}{c} This method generates a storyline \\ for an event using summaries at \\ different levels, but it needs \\ separate deep learning module \\ that can be trained to combat \\ noise in tweets. \\ \end{tabular} \\ \hline
[26] & \begin{tabular}{c} Time complexity, \\ fluency, and \\ adequacy \\ \end{tabular} & \begin{tabular}{c} summary- \\ aware \\ attention \\ \end{tabular} & Generic & \begin{tabular}{c} LCSTS dataset \\ ([[http://icrc.hitsz.edu](http://icrc.hitsz.edu)]([http://icrc.hitsz.edu](http://icrc.hitsz.edu)). \\ CR/Article/show/13/ \\ 9.html) (accessed on \\ 7 August 2023) \\ \end{tabular} & \
\begin{tabular}{c} Automatic and human \\ ROUGE-1 = 40.3 \\ ROUGE-2 = 27.8 \\ ROUGE-1 = 38.4 \\ \end{tabular} &
\begin{tabular}{c} Sega baselines and generates \\ summaries in a coherent manner, \\ but increases the \\ computational cost. \\ \end{tabular} \\ \hline
[50] & \begin{tabular}{c} Predict the cosine \\ score \\ \end{tabular} & Neural model & Specific event & \begin{tabular}{c} TSSubBERT \\ ([[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/)) \\ JoeBBS/R \\ TSSubBERT) \\ \end{tabular} (accessed on 7 August 2023) & \
\begin{tabular}{c} TSSubBERT \\ ROUGE-2 = 0.018 \\ COS Embed = 0.741 \\ [PERSON] et al. dataset \\ \end{tabular} &
\begin{tabular}{c} TSSubBERT \\ \(\,\) A neural model automatically \\ summarizes enormous Twitter \\ streams, but it needs to learn the \\ thresholds used in the \\ filtering step. \\ filtering step. \\ \end{tabular} \\ \hline
[37] & \begin{tabular}{c} Detecting \\ contextual bullying \\ \end{tabular} & \begin{tabular}{c} hybrid deep \\ learning \\ \end{tabular} & \begin{tabular}{c} Psychology \\ (cyber-bullying) \\ \end{tabular} & \begin{tabular}{c} Collected Twitter \\ dataset \\ \end{tabular} & \
\begin{tabular}{c} Accuracy = 90.45\% \\ Precision = 99.52\% \\ Recall = 88.98\% \\ F1 score = 99.25\% \\ Specificity = 90.94\% \\ \end{tabular} &
\begin{tabular}{c} It did not evaluate the reduction \\ in redundancy. \\ \end{tabular} \\ \hline
[33] & \begin{tabular}{c} Accuracy and \\ redundancy \\ \end{tabular} & \begin{tabular}{c} open-sourced \\ search engine \\ and a large \\ language \\ model \\ \end{tabular} & \begin{tabular}{c} Emergency \\ events \\ \end{tabular} & \begin{tabular}{c} TREC and \\ CrisisFACTS \\ \end{tabular} & \
\begin{tabular}{c} Manual \\ BERTScore = 0.4591 \\ ROUGE-2 = 0.0581 \\ \end{tabular} &
\begin{tabular}{c} It did not evaluate the reduction \\ in redundancy. \\ \end{tabular} \\ \hline
[35] & \begin{tabular}{c} Lecture \\ redundancy \\ \end{tabular} & \
\begin{tabular}{c} Event \\ \end{tabular} &
\begin{tabular}{c} ECOUGE-1 (F1 score: \\ 0.06 to 0.26, recall: 0.1 \\ 0.21, and precision: \\ 0.05 to 0.19) \\ \end{tabular} \\ \hline
[35] & \begin{tabular}{c} Time, similarity, \\ and efficiency \\ \end{tabular} & \begin{tabular}{c} Latent \\ Dirichlet \\ allocation (LDA) method \\ and K-means \\ clustering \\ \end{tabular} & \begin{tabular}{c} COVID19 Vaccine \\ dataset \\ \end{tabular} & \begin{tabular}{c} Collected Twitter \\ dataset \\ \end{tabular} & \
\begin{tabular}{c} Conducted Twitter \\ dataset \\ \end{tabular} &
\begin{tabular}{c} The ROUGE may not be the \\ most suitable evaluation matrix \\ for assessing extractive \\ summaries’ effectiveness. \\ \end{tabular} \\ \hline
[36] & \begin{tabular}{c} Accuracy and \\ relevance \\ \end{tabular} & \begin{tabular}{c} Combination \\ extraction and \\ algorithms \\ \end{tabular} & Events & Collected Instagram & \
\begin{tabular}{c} Accuracy = 7595 \\ Completeness = 82\% \\ Precision = 80\% \\ Harmonic mean = 81\% \\ Error rates = 25\% \\ \end{tabular} &
\begin{tabular}{c} The accuracy and efficiency of \\ the summarization process can \\ be enhanced. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}