science

Multi-document summarization

Multi-document summarization is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Multi-document summarization rather than just read about it. In short: Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.

Key takeaways

Multi-document summarization belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Multi-document summarization to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Multi-document summarization from memory before moving on to harder problems.

Reference excerpt

Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents. In such a way, multi-document summarization systems are complementing the news aggregators performing the next step down the road of coping with information overload.

Key benefits and difficulties Multi-document summarization creates information reports that are both concise and comprehensive. With different opinions being put together & outlined, every topic is described from multiple perspectives within a single document. While the goal of a brief summary is to simplify information search and cut the time by pointing to the most relevant source documents, comprehensive multi-document summary should in theory contain the required information, hence limiting the need for accessing original files to cases when refinement is required. In practice, it is hard to summarize multiple documents with conflicting views and biases. In fact, it is almost impossible to achieve clear extractive summarization of documents with conflicting views. Abstractive summarization is the preferred venue in this case. Automatic summaries present information extracted from multiple sources algorithmically, without any editorial touch or subjective human intervention, thus making it completely unbiased. The difficulties remain, if doing automatic extractive summaries of documents with conflicting views.

Technological challenges The multi-document summarization task 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. The Document Understanding Conferences, conducted annually by NIST, have developed sophisticated evaluation criteria for techniques accepting the multi-document summarization challenge. An ideal multi-document summarization system not only shortens the source texts, but also presents information organized around the key aspects to represent diverse views. Success produces an overview of a given topic. Such text compilations should also follow basic requirements for an overview text compiled by a human. The multi-document summary quality criteria are as follows:

clear structure, including an outline of the main content, from which it is easy to navigate to the full text sections text within sections is divided into meaningful paragraphs gradual transition from more general to more specific thematic aspects good readability. The latter point deserves an additional note. Care is taken to ensure that the automatic overview shows:

no paper-unrelated "information noise" from the respective documents (e.g., web pages) no dangling references to what is not mentioned or explained in the overview no text breaks across a sentence no semantic redundancy.

Real-life systems The multi-document summarization technology is now coming of age - a view supported by a choice of advanced web-based systems that are currently available.

ReviewChomp presents summaries of customer reviews for any given product or service. Some products have thousands of online reviews which renders the reviews unreadable by humans in real time. Search for the product or service is performed by the website. Ultimate Research Assistant - performs text mining on Internet search results to help summarize and organize them and make it easier for the user to perform online research. Specific text mining techniques used by the tool include concept extraction, text summarization, hierarchical concept clustering (e.g., automated taxonomy generation), and various visualization techniques, including tag clouds and mind maps. iResearch Reporter - Commercial Text Extraction and Text Summarization system, free demo site accepts user-entered query, passes it on to Google search engine, retrieves multiple relevant documents, produces categorized, easily readable natural language summary reports covering multiple documents in retrieved set, all extracts linked to original documents on the Web, post-processing, entity extraction, event and relationship extraction, text extraction, extract clustering, linguistic analysis, multi-document, full text, natural language processing, categorization rules, clustering, linguistic analysis, text summary construction tool set. Newsblaster is a system that helps users find news that is of the most interest to them. The system automatically collects, clusters, categorizes, and summarizes news from several sites on the web (CNN, Reuters, Fox News, etc.) on a daily basis, and it provides users an interface to browse the results. NewsInEssence may be used to retrieve and summarize a cluster of articles from the web. It can start from a URL and retrieve documents that are similar, or it can retrieve documents that match a given set of keywords. NewsInEssence also downloads news articles daily and produces news clusters from them. NewsFeed Researcher is a news portal performing continuous automatic summarization of documents initially clustered by the news aggregators (e.g., Google News). NewsFeed Researcher is backed by a free online engine covering major events related to business, technology, U.S. and international news. This tool is also available in on-demand mode allowing a user to build a summaries on selected topics. Scrape This is like a search engine, but instead of providing links to the most relevant websites based on a query, it scrapes the pertinent information off of the relevant websites and provides the user with a consolidated multi-document summary, along with dictionary definitions, images, and videos. JistWeb is a query specific multiple document summariser. As auto-generated multi-document summaries increasingly resemble the overviews written by a human, their use of extracted text snippets may one day face copyright issues in relation to the fair use copyright concept.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Multi-document summarization

Start with the simplest possible case. Write down what Multi-document summarization claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Multi-document summarization before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Multi-document summarization ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Multi-document summarization

In research: Multi-document summarization appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Multi-document summarization in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Multi-document summarization is common in secondary-school and first-year university syllabi. It links to neighbouring topics Information retrieval genres, Natural language processing, so understanding it makes those chapters shorter.
In everyday life: Look for Multi-document summarization outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Multi-document summarization” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Multi-document summarization in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Multi-document summarization means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Multi-document summarization out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Multi-document summarization in simple terms?

Why does Multi-document summarization matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Multi-document summarization?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Multi-document summarization.