Source-linked AI summary
Text Summarization Techniques: A Brief Survey
Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut
TL;DR
The paper addresses the challenge of producing concise summaries from overwhelming online information. It reviews dominant extractive summarization approaches and concludes that it provides insight into recent trends and progress in automatic summarization.
Problem
Automatic summarization is difficult because computers lack the human knowledge and language capability needed to preserve key information and meaning.
Method
The paper reviews dominant extractive approaches for single- and multi-document summarization, including topic representation, frequency-driven, graph-based, and machine learning methods.
Results
The review describes the effectiveness and shortcomings of major automatic summarization methods and reports that dependency-aware machine learning methods often outperform other techniques.
Takeaways & Limitations
The survey provides insight into recent trends and progress in extractive automatic summarization methods.
Takeaways & Limitations
Word-probability techniques depend on stop-word lists, whose contents are not straightforward to determine, motivating more advanced weighting methods.
Abstract
from arXiv · showhide
In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.
1 INTRODUCTION
The introduction frames automatic text summarization as a response to information overload and defines it as producing concise, fluent summaries that preserve key content and meaning. It distinguishes extractive from abstractive methods and states that the paper focuses on dominant extractive approaches.
- The dramatic growth of Internet documents has overwhelmed people with online information, motivating research into automatic text summarization.
- Automatic summarization produces concise, fluent summaries while preserving key information and overall meaning, with applications including search-engine snippets and news headlines.
- Automatic summarization is challenging because computers lack the human knowledge and language capability used to understand texts and identify their main points.
- Extractive methods select important text sections verbatim, whereas abstractive methods generate important material anew using advanced natural language techniques.
- The paper focuses on extractive summarization and reviews some of its most dominant approaches.
2 EXTRACTIVE SUMMARIZATION
Extractive summarization selects important sentences from one or more documents through intermediate representation, sentence scoring, and summary selection. Systems use topic- or indicator-based representations and may optimize selection for importance, coherence, and low redundancy.
- 2 EXTRACTIVE SUMMARIZATION: Extractive summaries select a subset of the most important sentences from a single document or multiple documents.
- 2 EXTRACTIVE SUMMARIZATION: Summarization systems construct an intermediate representation, score sentences, and select a summary containing a number of sentences.
- 2 EXTRACTIVE SUMMARIZATION: Intermediate representations divide summarization methods into topic representation and indicator representation approaches.Topic representation includes frequency-driven approaches, topic word approaches, latent semantic analysis, and Bayesian topic models; indicator representations describe sentences through importance features.
- 2 EXTRACTIVE SUMMARIZATION: Sentence importance scores reflect topic coverage or aggregated indicator evidence, with machine learning often used to determine indicator weights.
- 2 EXTRACTIVE SUMMARIZATION: Summarizers select the top k sentences, using greedy algorithms or optimization to maximize importance and coherency while minimizing redundancy.
3 TOPIC REPRESENTATION APPROACHES
This section surveys widely used topic-representation approaches for text summarization, including topic signatures, word weighting, centroid-based methods, LSA, and probabilistic topic models.
- 3 TOPIC REPRESENTATION APPROACHES: Topic representation approaches identify words or latent structures that describe document topics and support sentence importance estimation.The section introduces common approaches for representing topics in summarization.
- Topic words: Topic signatures identify descriptive words using frequency thresholds or log-likelihood ratios, and their use improved multi-document summarization accuracy in news.Sentence importance can be scored by the number or density of topic signatures, producing different preferences for longer sentences.
- Word weighting: Word weighting commonly uses word probability or TFIDF to determine how strongly words correlate with a topic.Word probability divides a word’s occurrences by all words in the input, while TFIDF is easy and fast to compute and widely used by summarizers.
- Centroid-based summarization: Centroid-based summarization clusters documents using TFIDF vectors, represents clusters as pseudo-documents, and ranks sentences by centrality and redundancy.It uses cluster-based relative utility (CBRU) for topic relevance and cross-sentence informational subsumption (CSIS) for redundancy.
- Latent semantic analysis: LSA builds a term-sentence matrix and uses topic weights to determine how strongly sentences represent topics for single- and multi-document summarization.The original strategy selected one sentence per topic, although a topic may require more than one sentence to convey its information.
- Probabilistic topic models: Probabilistic topic models address limitations of sentence-independent and heuristic scoring methods by representing documents as mixtures of latent topics.LDA is described as an unsupervised technique in which each topic is a probability distribution over words.
4 KNOWLEDGE BASES AND AUTOMATIC SUMMARIZATION
Knowledge bases and ontologies offer new possibilities for automatic text summarization by improving semantic representation. Existing methods often produce summaries with unsatisfactory soundness and readability because they overlook word semantics.
- Knowledge bases and automatic summarization: Existing summarization techniques often produce summaries that fail to cover all semantically relevant aspects effectively.The passage identifies unsatisfactory soundness and readability as consequences.
- Knowledge bases and automatic summarization: Knowledge bases and ontologies such as Wikipedia, YAGO, and DBpedia have opened further possibilities in text summarization.Their use in summarization has received increasing attention recently.
- Knowledge bases and automatic summarization: One approach maps sentences to ontology concepts so ontology features improve their semantic representation.Improved semantic representation is described as beneficial for summarization.
- Knowledge bases and automatic summarization: A Wikipedia-based method builds a bipartite sentence-concept graph and applies iterative ranking to select summary sentences.The method combines Wikipedia with graph-based ranking.
5 THE IMPACT OF CONTEXT IN SUMMARIZATION
Summarization systems can use contextual evidence beyond the target document to identify important topics. Examples include linked web pages, citing papers, and email-thread structure, though web pages pose challenges because their textual information is often scarce.
- Contextual evidence: Additional evidence, including blog discussions, comments, cited papers, and conference information, can help identify the most important or interesting topics.Such evidence supplements the content of the document being summarized.
- Web-page context: Web-page summarization is limited by non-textual elements and scarce textual information, but pages linking to the target can provide additional material.The earliest cited research queried web search engines for pages linking to the target page.
- Scientific-paper context: Citation-based summarization identifies important aspects of a scientific paper by extracting sentences in which other papers cite it.Mei et al. proposed a language model that assigns probabilities to words in citation-context sentences and scores original-paper sentences using KL divergence.
- Email context: Email summarization must account for interactive dialogue and thread structure, reflecting characteristics of both spoken conversation and written text.Methods summarized thread levels, used root-context overlap, incorporated thread and email-structure features, or clustered mailbox messages into topical groups.
6 INDICATOR REPRESENTATION APPROACHES
Indicator representation approaches rank sentences using feature-based representations rather than modeling input-text topics, commonly through graph methods and machine learning. Graph methods identify central sentences, while machine-learning classifiers score sentences but require labeled training data and may benefit from modeling sentence dependencies.
- Indicator approaches use features to rank important sentences directly, commonly employing graph-based methods and machine-learning techniques.These approaches model text representation instead of representing input-text topics.
- Graph methods represent documents as connected graphs whose sentence vertices are linked when similarity exceeds a threshold, often using cosine similarity with TFIDF weights.Graph partitions can represent discrete topics, while highly connected sentences are more likely to enter the summary.
- Graph-based summarization supports single- and multi-document settings and multiple languages, but TFIDF similarity omits syntactic and semantic information.Its language portability requires only sentence and word boundary detection beyond the graph method itself.
- Machine-learning approaches formulate summarization as classification, using learned probabilities from labeled documents and extractive summaries to distinguish summary from nonsummary sentences.The classifier’s probability that a sentence belongs to the summary serves as its sentence score.
- Hidden Markov models and Conditional Random Fields often outperform classifiers that decide sentence inclusion independently by explicitly modeling dependencies between sentences.Other common techniques include Naive Bayes, decision trees, and support vector machines.
- A primary limitation of supervised learning is the need for labeled training documents, which may not be readily available.Proposed alternatives include creating annotated corpora to expand public benchmarks and reduce overfitting risk.
7 EVALUATION
Summary evaluation is difficult because no ideal summary or standard metric exists, while human evaluators show low agreement. Evaluation therefore combines human judgments with automatic metrics, especially ROUGE, despite challenges in identifying preserved information and assessing readability.
- Evaluation challenges: No ideal summary or standard evaluation metric exists, making summary evaluation difficult and challenging.Human summarizers also show low agreement when evaluating and producing summaries.
- Evaluation campaigns: Evaluation campaigns including SUMMAC, DUC, and TAC helped design evaluation standards using human and automatic scoring.SUMMAC ran from 1996-1998, DUC from 2000-2007, and TAC from 2008-present.
- Evaluation challenges: Automatic evaluation must identify important information despite disparate expressions and assess grammaticality and coherence.These are among three major difficulties in automatic summary evaluation.
- Human evaluation: Human judges assess coverage in DUC and query answering in TAC, while considering grammaticality and non redundancy.The supplied passage truncates the list of scoring factors after non redundancy.
- Automatic evaluation: ROUGE is the most widely used automatic metric and compares candidate summaries with human reference summaries.ROUGE includes ROUGE-n, a recall-based n-gram measure, and ROUGE-L, which uses the longest common subsequence.
8 CONCLUSIONS
The paper concludes that information overload creates a strong need for automatic summarization tools and reviews major extractive approaches for single- and multi-document summarization. It offers insight into recent trends and progress while acknowledging that it cannot comprehensively cover all algorithms and approaches.
- Information overload makes it difficult for humans to summarize large amounts of text, creating an immense need for automatic summarization tools.
- The paper emphasizes extractive approaches for both single-document and multi-document summarization.
- Reviewed methods include topic representation, frequency-driven, graph-based, and machine learning techniques, offering insight into recent trends and progress.
- The paper does not comprehensively explain all diverse summarization algorithms and approaches.