# Back-of-the-Book Index Automation for Arabic Documents

arXiv: https://arxiv.org/abs/2410.10286 · HTML: https://mlsanity.com/paper/2410.10286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Description and Evaluation of Semantic Similarity Measures Approaches](https://mlsanity.com/paper/1310.8059/) (2013)

## Abstract (arXiv)

Back-of-the-book indexes are crucial for book readability. Their manual creation is laborious and error prone. In this paper, we consider automating back-of-the-book index extraction for Arabic books to help simplify both the creation and review tasks. Given a back-of-the-book index, we aim to check and identify the accurate occurrences of index terms relative to the associated pages. To achieve this, we first define a pool of candidates for each term by extracting all possible noun phrases from paragraphs appearing on the relevant index pages. These noun phrases, identified through part-of-speech analysis, are stored in a vector database for efficient retrieval. We use several metrics, including exact matches, lexical similarity, and semantic similarity, to determine the most appropriate occurrence. The candidate with the highest score based on these metrics is chosen as the occurrence of the term. We fine-tuned a heuristic method, that considers the above metrics and that achieves an F1-score of .966 (precision=.966, recall=.966). These excellent results open the door for future work related to automation of back-of-the-book index generation and checking.