# Multi-document abstractive summarization using ILP based multi-sentence compression

arXiv: https://arxiv.org/abs/1609.07034 · HTML: https://mlsanity.com/paper/1609.07034/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2016.

## Built on by (most cited first, 11 of 11)

- [Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles](https://mlsanity.com/paper/2309.09369/) (2023)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning](https://mlsanity.com/paper/2205.08221/) (2022)
- [Controlled Text Reduction](https://mlsanity.com/paper/2210.13449/) (2022)
- [Monolingual versus Multilingual BERTology for Vietnamese Extractive Multi-Document Summarization](https://mlsanity.com/paper/2108.13741/) (2021)
- [TWAG: A Topic-Guided Wikipedia Abstract Generator](https://mlsanity.com/paper/2106.15135/) (2021)
- [DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions](https://mlsanity.com/paper/2106.05365/) (2021)
- [Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge](https://mlsanity.com/paper/2202.07138/) (2022)
- [Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature](https://mlsanity.com/paper/2203.15959/) (2022)
- [Semantic Extractor-Paraphraser based Abstractive Summarization](https://mlsanity.com/paper/2105.01296/) (2021)
- [Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics](https://mlsanity.com/paper/2108.12822/) (2021)

## Abstract (arXiv)

Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences. Second, we generate K-shortest paths from the sentences in each cluster using a word-graph structure. Finally, we select sentences from the set of shortest paths generated from all the clusters employing a novel integer linear programming (ILP) model with the objective of maximizing information content and readability of the final summary. Our ILP model represents the shortest paths as binary variables and considers the length of the path, information score and linguistic quality score in the objective function. Experimental results on the DUC 2004 and 2005 multi-document summarization datasets show that our proposed approach outperforms all the baselines and state-of-the-art extractive summarizers as measured by the ROUGE scores. Our method also outperforms a recent abstractive summarization technique. In manual evaluation, our approach also achieves promising results on informativeness and readability.