What is Multi-document Summarization?
Multi-document summarization is an automatic procedure aimed at extracting information from multiple texts written about the same topic. The goal is to create a summary report that allows users to quickly familiarize themselves with the information contained in a large cluster of documents. This process is particularly useful in situations where there is an overwhelming amount of related or overlapping documents, such as various news articles reporting the same event, multiple reviews of a product, or pages of search results in search engines.
There are two main approaches to multi-document summarization: extractive and abstractive. Extractive summarization systems aim to extract salient snippets, sentences, or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.
The task of multi-document summarization is more complex than summarizing a single document, even a long one. The difficulty arises from thematic diversity within a large set of documents. A good summarization technology aims to combine the main themes with completeness, readability, and concision.
However, it's important to note that in practice, it can be challenging to summarize multiple documents with conflicting views and biases. Despite these challenges, multi-document summarization has the potential to create information reports that are both concise and comprehensive, providing multiple perspectives on a topic within a single document.
Various methods and models have been developed to tackle this task, including deep learning techniques that can generate more comprehensive and accurate summaries from a cluster of topic-related documents.
What are some challenges in multi-document summarization?
Multi-document summarization (MDS) presents several challenges:
Handling Large Inputs — MDS often deals with extremely long inputs, which can be challenging to process and summarize effectively.
Thematic Diversity — The task becomes more complex due to the thematic diversity within a large set of documents. A good summarization technology aims to combine the main themes with completeness, readability, and concision.
Redundancy and Repetition — MDS systems often struggle with issues of redundancy and repetition. They need to identify salient content units that should be summarized and avoid repeating the same information.
Bias and Quality Control — Ensuring the quality of output and avoiding bias in the summarization process is another significant challenge. This is particularly important when dealing with an unbiased corpus.
Inter-document Relationships — Understanding and leveraging the relationships between different documents can be difficult but is crucial for effective MDS.
Conflict Handling — MDS systems often struggle to handle conflicts in source documents, which can lead to inaccuracies or inconsistencies in the generated summaries.
Evaluation — Evaluating the performance of MDS systems can be challenging due to the subjective nature of summarization. Traditional evaluation techniques utilize both qualitative and quantitative metrics, but these may not fully capture the quality of the generated summaries.
Computational Resources — MDS requires significant computational resources, both in terms of memory and processing power, which can be a limiting factor, especially for large-scale applications.