# New Alignment Methods for Discriminative Book Summarization

arXiv: https://arxiv.org/abs/1305.1319 · HTML: https://mlsanity.com/paper/1305.1319/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2013.

## Built on by (most cited first, 18 of 18)

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- [Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation](https://mlsanity.com/paper/2510.21891/) (2025)
- [Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification](https://mlsanity.com/paper/2410.02930/) (2024)
- [Efficient Zero-Shot Long Document Classification by Reducing Context Through Sentence Ranking](https://mlsanity.com/paper/2508.17490/) (2025)
- [MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees](https://mlsanity.com/paper/2608.20636/) (2026)

## Abstract (arXiv)

We consider the unsupervised alignment of the full text of a book with a human-written summary. This presents challenges not seen in other text alignment problems, including a disparity in length and, consequent to this, a violation of the expectation that individual words and phrases should align, since large passages and chapters can be distilled into a single summary phrase. We present two new methods, based on hidden Markov models, specifically targeted to this problem, and demonstrate gains on an extractive book summarization task. While there is still much room for improvement, unsupervised alignment holds intrinsic value in offering insight into what features of a book are deemed worthy of summarization.