# Long document summarization using page specific target text alignment and distilling page importance

arXiv: https://arxiv.org/abs/2509.16539 · HTML: https://mlsanity.com/paper/2509.16539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [Efficient Attentions for Long Document Summarization](https://mlsanity.com/paper/2104.02112/) (2021)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [Sparse Sinkhorn Attention](https://mlsanity.com/paper/2002.11296/) (2020)
- [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://mlsanity.com/paper/1909.03186/) (2019)
- [An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics](https://mlsanity.com/paper/2207.00939/) (2022)
- [Poolingformer: Long Document Modeling with Pooling Attention](https://mlsanity.com/paper/2105.04371/) (2021)
- [Hierarchical Learning for Generation with Long Source Sequences](https://mlsanity.com/paper/2104.07545/) (2021)
- [Noisy Self-Knowledge Distillation for Text Summarization](https://mlsanity.com/paper/2009.07032/) (2020)
- [Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization](https://mlsanity.com/paper/2408.15801/) (2024)

## Built on by (most cited first, 1 of 1)

- [ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog](https://mlsanity.com/paper/2607.04438/) (2026)

## Abstract (arXiv)

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.