It might be derived from the inconsistent representation of sentence embeddings, anisotropic distributions of token generation, and a narrow subset of the entire representation space [16; 17; 18]. When the distance between different tokens in a representation space is close, these tokens have high cosine similarity. A showcase reveals that cosine similarities between tokens within a sentence could be larger than 0.95, and therefore, duplicate tokens will be unavoidably generated at different stages [19]. To solve or to relieve this degradation problem, massive attempts have been made. One feasible way is mapping the generated sentence vectors into an isotropic and uniform distribution space. For instance, BERT-flow [20] turns the sentence representations from BERT encoder into a smooth and isotropic Gaussian distribution space using a reversible flow transformation. It achieves significant improvement on several semantic textural similarity tasks. [PERSON] and his colleagues [21] design a dual-stream attention mechanism and use a positional residual strategy to improve the robustness of extractive summarization. A summary method based on two-layer Transformer in [22] employs BART (bidirectional and autoregressive Transformer) [23] and T5 (text-to-text transfer converter) [24] to ensure the summary coherence. Another promising way is from contrastive learning (CL). Traditional text augmentation is used to construct positive and negative sample pairs from the augmented sentence set. Its training objective becomes making the embeddings of positive sample pairs closer and the distance of negative pairs farther. Debiased CL [25] is this kind of approach that samples appropriate same-label data points, since negative pairs sampledfrom different labels or classes improve performance [26], achieving consistent improvement on language, vision and reinforcement learning benchmarks. The contrastive learning for sentence representation (CLEAR) method [27] employs multiple sentence-level augmentation strategies, and during pre-training, different sentence augmentation strategies result in improvement on specific tasks. Token-aware CL (TaCL) [18] is a continual pre-training approach that is fully unsupervised and requires no additional samples. It embraces a teacher model and a student model, and both are initialized with the same pre-trained BERT. The objective function contains a masked language modeling term, a next sentence prediction term and a token-aware contrastive learning term for learning an isotropic and discriminative distribution of token representations. For reducing the impact of summary false negatives and effectively maintaining spatial consistency, a metric score is employed to dynamically penalize positive and negative samples during model training [28]. In extractive multi-document summarization, a contrastive hierarchical discourse graph is designed to capture complex discourse relationships and global topic coherence, and it shows excellent performance [29]. In any case, compared to greedy search (GS) [30] and nucleus search (NS) [31] decoding methods, some other decoding methods seem more promising to relieve this anisotropy problem [32]. For instance, a contrastive search (CS) decoding method injects CL into the text decoding stage, and its performance is verified to be better than traditional decoding methods [19]. On open-ended text generation, an empirical study [33] of CS and contrastive decoding indicates that CS substantially outperforms contrastive decoding in terms of the diversity and coherence metrics. The fidelity-enriched contrastive search (FECS) method [34] augments the CS framework with context-aware regularization terms, and in both abstractive summarization and dialogue generation tasks, it has been confirmed to improve semantic coherence among tokens, mitigate repetition, and strengthen fidelity to the provided source labels in the generated output.