By storing data on text attributes comprising a plurality of attribute items, a text analysis rule, a plurality of text type identification rules, a plurality of text content/domain type identification rules, a plurality of summarization method setting tables, and a summarization rule in a storage, analysing an inputted text in an electronic form on the basis of the text analysis rule and the data on text attributes, generating a text analysis table, determining the text type of the inputted text in an electronic form on the basis of the text type identification rules and the content of the text analysis table, also determining the text content/domain type on the basis of the text content/domain type identification rules, selecting a summarization method setting table corresponding to a combination of the determined text type and text content/domain type, and summarizing the inputted text in an electronic form on the basis of the summarization method setting table and the summarization rule, a text summarizing method and system for preventing the correctness of text summarization from dropping due to a difference in the constitution, field, and content of a text.
A text message is first parsed into its constituent semantic components such as header fields and body components. Then, different compression methods may be performed on each semantic component depending on the importance of the semantic component, the context of the semantic component, the characteristics of the semantic component, and whether or not the semantic component uses natural language expressions. For example, it is determined what compression method, if any, is to be performed on the semantic component. Each semantic component may be compressed individually. Since text compression takes the unique features of each semantic component into consideration rather than considering the text message as a monolithic text unit, a more intuitive text compression results.
Only important portions in all of messages are extracted in the halfway or after the end of a discussion and are allowed to be referred to the user. The time for discussion is reduced, thereby smoothly proceeding a conference. An electronic conference system is constructed in an electronic conference system server in a manner such that a plurality of user clients are connected to the electronic conference system server through a network (not shown). The electronic conference system is made up of a message database, a message relation extracting unit, a message type setting unit, a message input and display unit, and an electronic conference summarizing system. The electronic conference summarizing system is constructed by a discussion path specifying unit, a message merging unit, a duplication message deleting unit, and a message summary file. All of the conclusions derived from a subject and its discussion tree are extracted, unnecessary portions such as quotations or the like are deleted, and the correspondence of questions and answers is arranged and is formed as a summary.
A technique for compressing texts such that referential integrity, sentence coherency, punctuation and readability are preserved and which provides for compression of sentence constituents based on the type of content, the informativity of the sentence constituent and the grammatical readability of the resultant sentence or phrase. Information content portions are parsed to generate parts of speech tags. The informativity of the constituents in a phrase or sentence is determined and the parts of speech having lower information content and having a low effect on grammatical readability of the phrase or sentence are selectively compressed. Parts of speech having successively higher informativity and low effect on grammatical readability are selected for compression until the desired level of compression is reached. Compressed portions are indicated in the summary with a selectable placeholder which expands to display the compressed text.
A text message is first parsed into its constituent semantic components such as header fields and body components. Then, different compression methods may be performed on each semantic component depending on the importance of the semantic component, the context of the semantic component, the characteristics of the semantic component, and whether or not the semantic component uses natural language expressions. For example, it is determined what compression method, if any, is to be performed on the semantic component. Each semantic component may be compressed individually. Since text compression takes the unique features of each semantic component into consideration rather than considering the text message as a monolithic text unit, a more intuitive text compression results.
A text input section (1) divides an inputted text into sentences and attaches a number to each of the sentences, which is stored in a text data base together with the number. An important word recognizing section (2) generates a list of important words for each sentence to store it in a storing section (8). An important word weighting section (3) weights each important word. A relation degree computing section (4) computes a relation degree between an attention sentence and a precedent sentence. An important degree computing section (5) computes an importance degree of each attention sentence. A tree structure determining section (6) determines a parent sentence of the attention sentence and determines a tree structure of the inputted text. Unlike the case of determining whether or not character strings of key words are merely coincident with each other, it is possible to determine a parent sentence of each sentence based on a degree of connection between two sentences and analyze a structure of the inputted text with high accuracy according to the above construction.
