Extract with Order for Coherent Multi-Document Summarization

The field of automatic multi-document summarization has evolved from simple heuristic-based methods to sophisticated graph and neural network models. Early approaches like LexRank used lexical overlap and PageRank algorithms to identify salient sentences, while later techniques incorporated submodular functions for coverage and diversity. Recent deep learning models, such as sequence-to-sequence architectures, have shown promise but often struggle with coherence and redundancy. Despite these advances, balancing informativeness, diversity, and coherence remains challenging, especially in multi-source scenarios where content overlap and topic shifts complicate the summarization process. Researchers have explored entity-based coherence models and graph linkages, but integrating semantic understanding with content optimization is still an open problem.

The core challenge in multi-document summarization is to generate summaries that are both highly informative and logically coherent. Existing systems tend to favor importance or coverage, often at the expense of readability. The difficulty lies in selecting sentences that collectively cover key topics, avoid redundancy, and are arranged in a way that makes sense to human readers. Traditional ordering strategies based on sentence position or lexical overlap are insufficient in multi-source contexts, where sentence relevance varies widely, and positional cues are unreliable. Therefore, developing a unified framework that simultaneously optimizes content relevance and coherence is crucial.

This work introduces several key innovations: 1) combining continuous word embeddings with keyphrase importance scores for sentence evaluation, 2) employing ILP to optimize sentence selection under multiple constraints, 3) leveraging hierarchical clustering to reduce redundancy and enhance diversity, 4) proposing a semantic similarity-based greedy algorithm for sentence ordering that maximizes coherence. These innovations address the limitations of prior models that rely solely on lexical overlap or positional heuristics, enabling a more nuanced understanding of content relevance and flow. The integration of semantic relations into the ordering process is particularly novel, providing a significant step forward in producing human-like summaries.

• �� Preprocessing: Tokenization, POS tagging, stopword removal, lemmatization using NLTK.
• �� Sentence similarity: Compute sentence vectors by weighted sum of word embeddings; combine with entity overlap similarity.
• �� Sentence ranking: Use an enhanced TextRank algorithm incorporating semantic similarity (λ=0.3) to assign importance scores.
• �� Clustering: Hierarchical agglomerative clustering with complete linkage, similarity threshold τ=0.5, to group similar sentences.
• �� Sentence selection: ILP formulation maximizes sum of keyphrase weights and sentence importance, constrained by length L, with redundancy control.
• �� Sentence ordering: Greedy algorithm based on semantic similarity (λ=0.5), placing sentences to maximize coherence score.
• �� Final summary: Selected sentences are ordered and concatenated to produce the final extract.

The system was evaluated on the DUC 2004 dataset, focusing on Task 2 with a 100-word limit. Baseline comparisons included LexRank, GreedyKL, Submodular, and ICSISumm. Hyperparameters such as λ for similarity balancing and clustering thresholds were tuned via grid search. ROUGE-1, ROUGE-2, and ROUGE-SU4 scores measured informativeness, while coherence was assessed with a probabilistic model. Ablation studies isolated the impact of keyphrase integration and semantic ordering. The experiments demonstrated consistent improvements over baselines, validating the effectiveness of the combined semantic and optimization approach.

The proposed system achieved ROUGE-1 of 39.45, ROUGE-2 of 10.12, and ROUGE-SU4 of 14.09, surpassing all baselines. The coherence probability reached 0.68, indicating better sentence flow. Ablation showed that removing semantic similarity reduced scores by approximately 2 points, confirming its importance. The integration of keyphrases notably increased content coverage, especially for longer sentences, while the semantic ordering improved readability, as reflected in higher coherence scores. These results demonstrate the method’s robustness across different content types and document sets.

This framework is suitable for news aggregation, legal document synthesis, scientific report summarization, and any domain requiring concise, coherent multi-source content. It can be integrated into content management systems, automated news feeds, and legal analysis tools, providing users with quick, reliable summaries. The approach requires minimal manual tuning, making it adaptable for real-time applications and large-scale deployment. Its emphasis on semantic understanding ensures relevance even with diverse or conflicting sources.

The computational cost of ILP and semantic similarity calculations limits scalability to very large datasets. Dependence on RAKE for keyphrase extraction can introduce errors if keyphrases are poorly identified. The model assumes a relatively coherent topic set; highly diverse or conflicting documents may reduce effectiveness. Future work should focus on improving efficiency, robustness, and extending the approach to handle broader content variability.