🤖 AI Summary
To address the challenges of limited context windows and information redundancy in long-document summarization, this paper proposes a BART-based abstract summarization framework incorporating page-level alignment and importance-weighted scoring. The method first partitions documents into pages and introduces a page-level target text alignment mechanism to enable fine-grained supervision. Second, it designs a dynamic page importance weighting module that explicitly prioritizes semantically critical content. Third, it integrates page-wise encoding, an importance scoring network, and a partial-summary generation strategy to jointly optimize coherence and fidelity. Experiments on standard benchmarks demonstrate substantial improvements: ROUGE-1 and ROUGE-2 scores increase by 6.32% and 8.08%, respectively, surpassing state-of-the-art approaches. The framework effectively balances contextual coverage and salient information preservation, offering a principled solution for long-document abstraction under constrained transformer input lengths.
📝 Abstract
The rapid growth of textual data across news, legal, medical, and scientific domains is becoming a challenge for efficiently accessing and understanding large volumes of content. It is increasingly complex for users to consume and extract meaningful information efficiently. Thus, raising the need for summarization. Unlike short document summarization, long document abstractive summarization is resource-intensive, and very little literature is present in this direction. BART is a widely used efficient sequence-to-sequence (seq-to-seq) model. However, when it comes to summarizing long documents, the length of the context window limits its capabilities. We proposed a model called PTS (Page-specific Target-text alignment Summarization) that extends the seq-to-seq method for abstractive summarization by dividing the source document into several pages. PTS aligns each page with the relevant part of the target summary for better supervision. Partial summaries are generated for each page of the document. We proposed another model called PTSPI (Page-specific Target-text alignment Summarization with Page Importance), an extension to PTS where an additional layer is placed before merging the partial summaries into the final summary. This layer provides dynamic page weightage and explicit supervision to focus on the most informative pages. We performed experiments on the benchmark dataset and found that PTSPI outperformed the SOTA by 6.32% in ROUGE-1 and 8.08% in ROUGE-2 scores.