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Automatyczne tworzenie podsumowań tekstów metodami algebraicznymi

Gramacki J., Gramacki A.

Pomiary Automatyka Kontrola

2011

R. 57, nr 7

751-755

Duża liczba zwracanych (na przykład przez różnego rodzaju wyszukiwarki internetowe) dokumentów oznacza, że często zmuszeni jesteśmy do czasochłonnego ich przeglądania, celem weryfikacji trafności zwracanych wyników. Gdy dokumenty są długie, czas ich przeglądania znacznie się wydłuża. Można by go wydatnie skrócić, gdyby istniała możliwość automatycznego generowania sensownych podsumowań (streszczeń). W artykule omawiamy wybrane algebraiczne metody służące automatycznemu wydobywaniu z tekstu jego najistotniejszych słów kluczowych oraz najistotniejszych zdań.

Text summarization is a real practical problem due to explosion of the volume of textual information available nowadays. In order to solve this problem, text summarization systems which extract brief information from a given text are created. The end user, by looking only at the summary, may decide whether the document is or is not of interest to him/her. Built summaries can have 2 fundamental forms. Firstly, extractive summarization may collect important sentences from the input text to constitute the summary. Secondly, abstractive summarization tries to capture main concepts of the text and then some new sentences, summarizing the input text, are generated. Nowadays, however, it seems that the latter approach still needs extensive works to be really useful. A summary can be extracted from a single document or multiple documents. In the paper the authors build summaries of one document only. The extension into multi-document summaries is the straightforward task in the case when a set of semantically uniform texts is summarized. Summaries may also be categorized as generic and query-based summaries. In the first case, there are generated summaries con-taining main topics of a document. In the second case, summaries contain the sentences that are related to the given queries. In the paper there are built generic summaries. Summarization systems use different approaches to determine important sentences. Here there is used semantic oriented approach based on a method known as Latent Semantic Analysis (LSA). LSA is an algebraic method that extracts meaning of words and similarity of sentences using the information about usage of the words in the context. It uses Singular Value Decomposition (SVD) for finding semantically similar words and sentences. Using the results of SVD the authors try to select best sentences (which constitute the best summary of the text). The paper is organized as follows. In Section 2 there is formulated the problem. In Section 3 there is shown how a docu-ment may be represented in a useful algebraic format. The so called Term-Sentence matrix (TSM) is used. The authors also point at some preliminary tasks necessary to be performed for successful further analysis. In Subsection 3.2 there is shortly presented an idea of LSA as based on SVD decomposition. In the last section 4 two examples of text summarizations build for both Polish and English texts are given. The two methods used differ slightly from each other. The authors' extracting key words and key sentences seems to be proper content-related summaries of the input texts.

Sentence extraction using similar words

Suzuki Y., Fukumoto F.

Archives of Control Sciences

2005

Vol. 15, no. 4

665-672

In both written and spoken languages, we sometimes use different words in order to describe the same thing. For Instance, we use "candidacy and "running in an election" as the same meaning. This makes text classification, event tracking and text summarization difficult. In this paper, we propose a method to extract words which are semantically similar to each other accurately. Using this method, we extracted similar word pairs from newspaper articles. Furthermore, we performed sentence extraction from the newspaper articles using the extracted similar word pairs. We hypothesized that the headline is salient information of the newspaper article and presence of headline terms in the article can be used to detect salient sentences of news text. By using similar words in the headline, we obtained better results than that without using it. The results suggest that our method is useful for text summarization.