# Automatic Short Text Summarization Techniques in Social Media Platforms
## 6 Evaluation Metrics for Short Text Summarization  
Evaluation is a crucial aspect of short text summarization, as it allows researchers and practitioners to assess the quality and effectiveness of the generated summaries. Various evaluation metrics have been proposed to measure the performance of summarization models. In this section, we discuss some of the commonly used evaluation metrics for short text summarization [29; 30]:  
1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE is one of the most widely used evaluation metrics for text summarization. It measures the overlap between the n-grams (unigrams, bigrams, trigrams, etc.) of the generated summary and the reference summary. The ROUGE score includes metrics such as ROUGE-1 (unigram overlap), ROUGE-2 (bigram overlap), and ROUGE-L (longest common subsequence) [31].
2. BLEU (Bilingual Evaluation Understudy): Originally developed for machine translation, BLEU has been adapted for text summarization evaluation. BLEU compares the n-grams in the generated summary with those in the reference summary and calculates precision scores. It is especially useful for measuring the quality of abstractive summaries.
3. METEOR (Metric for Evaluation of Translation with Explicit Ordering): METEOR evaluates the quality of summaries by considering exact word matches as well as paraphrased and stemmed matches. It incorporates additional features like stemming, synonym matching, and word order similarity to assess the overall quality of the generated summaries.
4. CIDEr (Consensus-based Image Description Evaluation): Originally developed for image captioning, CIDEr has been adjusted to evaluate text summarization. It computes a consensus-based similarity score by comparing n-gram overlaps between the generated and reference summaries while also considering the diversity and quality of the generated summaries.
5. ROUGE-WE (ROUGE with word embeddings): ROUGE-WE enhances the traditional ROUGE metrics by incorporating word embeddings to assess the semantic similarity between the generated and reference summaries. It provides a more nuanced evaluation of the generated summaries' semantic quality.
6. BertScore: BertScore utilizes contextual embeddings from pre-trained models like BERT to measure the similarity between the generated and reference summaries. It takes into account the context of the words in the summaries, resulting in a more accurate evaluation [32].  
Human evaluation:  
In addition to automated metrics, human evaluation is essential for assessing the quality of summarization models. Human evaluators rank or rate the generated summaries based on their coherence, relevance, and informativeness compared to the reference summaries.  
Each evaluation metric has its strengths and limitations. ROUGE and BLEU are widely used due to their simplicity and ease of implementation. However, they may not fully capture the semantic quality of abstractive summaries. On the other hand, newer metrics like BertScore and ROUGE-WE address some of these limitations by considering semantic similarity. A comprehensive evaluation of short text summarization models should consider multiple metrics, including both automated and human evaluations, to obtain a holistic understanding of their performance. Each metric contributes valuable insights into different aspects of summarization quality, and a combination of these metrics can provide a more reliable assessment of the summarization models.