Patent Publication Number: US-2005131931-A1

Title: Abstract generation method and program product

Description:
BACKGROUND OF THE INVENTION  
      1. Field of the Invention  
      The present invention relates to an abstract generation method of generating an abstract from document information, such as an electronic patient chart, and a program product that implements the abstract generation method.  
      2. Description of the Related Art  
      When a large amount of document information is contained in one file, in order to make it possible to confirm the contents of each piece of document information with ease, an abstract is generated in many cases. For instance, a written abstract is generated separately using important parts excerpted from the document information or only the important parts in the document information are underlined or highlighted. With the abstract generated in this manner, it becomes possible to grasp the contents of each piece of document information with ease. In addition, it also becomes possible to extract desired document information from the file with ease.  
      When an abstract is generated from a document, such as an electronic patient chart, where the same expressions appear many times, it is effective that the abstract is generated by extracting sentences containing specific keywords. For instance, with a technique disclosed in JP H11-316762 A, an abstract of an e-mail is created by extracting sentences containing important expressions prepared in advance.  
      When sentences containing specific keywords are extracted in this manner, however, each sentence where its main part has the same contents but a clause expressing a date or a period, a conjunction, or the like is added before or after the main part is extracted. When an abstract is generated, however, such a clause expressing a date or a period, conjunction, or the like does not have a specifically important meaning and, if anything, makes the abstract difficult to read. Therefore, in order to generate an abstract that is easy to read and understand, it is preferable that only the main part of each sentence that does not contain a clause expressing a date or a period, a conjunction, or the like is concisely described in the abstract.  
     SUMMARY OF THE INVENTION  
      It is therefore an object of the present invention to provide an abstract creation method, with which it is possible to display only the main parts of sentences concisely and effectively, and a program product that implements the abstract creation method.  
      According to a first aspect of the present invention, there is provided an abstract generation method of generating an abstract from document information, characterized by including: extracting each sentence containing a keyword as a key-sentence from among sentences contained in the document information; comparing a key-sentence and another key-sentence with each other and judging whether a part of the key-sentence matches the other key-sentence; setting a summary candidate in accordance with a result of the judgment; and generating an abstract based on each part of the document information corresponding to the summary candidate. Here, when it is judged that a part of the key-sentence matches the other key-sentence, a character string in the matching part is set as the summary candidate, and when it is not judged that a part of the key-sentence matches the other key-sentence, the key-sentence is set as the summary candidate.  
      According to a second aspect of the present invention, there is provided an abstract generation method of generating an abstract from document information, characterized by including: comparing one sentence and another sentence contained in the document information with each other and judging whether a part of the sentence matches the other sentence; setting a simplified sentence candidate in accordance with a result of the judgment; extracting each simplified sentence candidate containing a keyword from among simplified sentence candidates and setting the extracted simplified sentence candidate as a summary candidate; and generating an abstract based on each part of the document information corresponding to the summary candidate. Here, when it is judged that a part of the sentence matches the other sentence, a character string in the matching part is set as the simplified sentence candidate, and when it is not judged that a part of the sentence matches the other sentence, the sentence is set as the simplified sentence candidate.  
      According to a third aspect of the present invention, there is provided a program product that gives a summary generation function to a computer, characterized by including: an extraction processing portion that extracts each sentence containing a keyword as a key-sentence from among sentences contained in document information; a judgment processing portion that compares a key-sentence and another key-sentence with each other and judges whether a part of the key-sentence matches the other key-sentence; a setting processing portion that sets a summary candidate in accordance with a result of the judgment by the judgment processing portion; and a generation processing portion that generates an abstract based on each part of the document information corresponding to the summary candidate set in the setting processing portion. Here, the setting processing portion includes processing that sets, when the judgment processing portion has judged that a part of the key-sentence matches the other key-sentence, a character string in the matching part as the summary candidate, and sets, when the judgment processing portion has not judged that a part of the key-sentence matches the other key-sentence, the key-sentence as the summary candidate.  
      According to a fourth aspect of the present invention, there is provided a program product that gives a summary generation function to a computer, characterized by including: a judgment processing portion that compares a sentence and another sentence contained in document information and judges whether a part of the sentence matches the other sentence; a simplification processing portion that sets a simplified sentence candidate in accordance with a result of the judgment by the judgment processing portion; a setting processing portion that extracts each simplified sentence candidate containing a keyword from among simplified sentence candidates set by the simplification processing portion and sets the extracted simplified sentence candidate as a summary candidate; and a generation processing portion that generates an abstract based on each part of the document information corresponding to the summary candidate set by the setting processing portion. Here, the simplification processing portion includes processing that sets, when the judgment processing portion has judged that a part of the sentence matches the other sentence, a character string in the matching part as the simplified sentence candidate, and sets, when the judgment processing portion has not judged that a part of the sentence matches the other sentence, the sentence as the simplified sentence candidate.  
      According to the aspects of the present invention, among sentences containing a keyword, each sentence including a clause expressing a date or a period like “after that” or “in a month”, a conjunction, or the like is simplified into a sentence, in which the clause, conjunction, or the like has been removed, and is set as a summary candidate. As a result, it becomes possible to generate a concise and effective abstract where each unnecessary expression, such as a clause expressing a date or a period or a conjunction, has been omitted.  
      It should be noted here that in the present invention, the term “sentence” refers to a character string delimited by a line feed mark and the next line feed mark as well as a character string delimited by a period “.” and the next period “.”, or other type of character string delimited by other method. Also, as one abstract creation form in the abstract generation, it is possible to adopt a form where document information is displayed in its entirety and marking is performed on each character part corresponding to a summary candidate set in the summary candidate setting. Here, the term “marking” refers to a technique with which differentiation of displaying is achieved by changing the weight, size, color, and/or the like of each character string as well as a technique with which the character string is prominently displayed through underlining or highlighting. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The above and other objects and novel features of the present invention will become apparent more completely from the following description of embodiments to be made with reference to the accompanying drawings, wherein:  
       FIG. 1  shows a construction of an abstract creation apparatus according to a first embodiment;  
       FIG. 2  is a flowchart showing a processing operation of the abstract creation apparatus according to the first embodiment;  
       FIG. 3A  shows a concrete example of an abstract creation operation according to the first embodiment;  
       FIG. 3B  shows the concrete example of the abstract creation operation according to the first embodiment;  
       FIG. 3C  shows the concrete example of the abstract creation operation according to the first embodiment;  
       FIG. 3D  shows the concrete example of the abstract creation operation according to the first embodiment;  
       FIG. 4  shows a construction of an abstract creation apparatus according to a second embodiment;  
       FIG. 5  is a flowchart showing a processing operation of the abstract creation apparatus according to the second embodiment;  
       FIG. 6A  shows a concrete example of an abstract creation operation according to the second embodiment;  
       FIG. 6B  shows the concrete example of the abstract creation operation according to the second embodiment;  
       FIG. 6C  shows the concrete example of the abstract creation operation according to the second embodiment;  
       FIG. 6D  shows the concrete example of the abstract creation operation according to the second embodiment;  
       FIG. 7A  shows a concrete example of an abstract creation operation according to a third embodiment;  
       FIG. 7B  shows the concrete example of the abstract creation operation according to the third embodiment;  
       FIG. 7C  shows the concrete example of the abstract creation operation according to the third embodiment;  
       FIG. 8  is a flowchart showing a processing operation of an abstract creation apparatus according to the third embodiment;  
       FIG. 9A  shows a concrete example of an abstract creation operation according to a fourth embodiment;  
       FIG. 9B  shows the concrete example of the abstract creation operation according to the fourth embodiment;  
       FIG. 9C  shows the concrete example of the abstract creation operation according to the fourth embodiment; and  
       FIG. 10  is a flowchart showing a processing operation of an abstract creation apparatus according to the fourth embodiment. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
      Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. It should be noted here that the following embodiments are merely examples of the present invention, and therefore there is no intention to specifically limit the scope of the present invention to the embodiments.  
     First Embodiment  
       FIG. 1  shows a construction of an abstract creation apparatus according to a first embodiment.  
      It should be noted here that in terms of hardware, it is possible to realize the abstract creation apparatus in this embodiment using an arbitrary computer CPU, memory, LSI, and the like. Also, in terms of software, it is possible to realize the abstract creation apparatus in this embodiment with a program or the like loaded into a memory and having a recording control function. Functional blocks of the abstract creation apparatus shown in  FIG. 1  are realized by hardware and software. Note that in order to realize these functional blocks, aside from the form where hardware and software are combined with each other, it is of course possible to use a form where only hardware or only software is used.  
      As shown in  FIG. 1 , the abstract creation apparatus includes a sentence input unit  101 , a morphological analysis unit  102 , a keyword setting unit  103 , a keyword dictionary  104 , a key-sentence extraction unit  105 , a summary candidate setting unit  106 , and a summary output unit  107 .  
      The sentence input unit  101  receives document information, such as an electronic patient chart, from an input port, a disk drive, or the like. The morphological analysis unit  102  includes a database for morphological analysis with which it divides document information (document information in one unit) inputted from the input unit  101  into morphemes through morphological analysis, gives punctuation information and information showing whether the morphemes are each an independent word or an adjunct to the document information, and outputs them to the keyword setting unit  103  and the key-sentence extraction unit  105 .  
      The keyword setting unit  103  detects the occurrence frequency of each independent word contained in the document information and stores each independent word, whose occurrence frequency is equal to or more than a predetermined threshold value, as a keyword candidate in a memory (not shown). When doing so, for the keyword candidate, a score corresponding to the occurrence frequency is set and is stored in the memory.  
      In the keyword dictionary  104 , each keyword candidate set by a user using an input means, such as a keyboard, in advance is stored. When the user sets the keyword candidate, he/she sets an importance for the keyword candidate. In the keyword dictionary  104 , a score corresponding to the importance is stored so as to be associated with the keyword.  
      The keyword setting unit  103  generates a keyword table from the keyword candidate stored in the memory and the keyword candidate registered in the keyword dictionary  104 . This keyword table is referred to at the time of key-sentence extraction by the key-sentence extraction unit  105 .  
      It should be noted here that for instance, the keyword table is generated from every keyword candidate registered in the keyword dictionary  104  and keyword candidates with several top-ranked scores among the keyword candidates stored in the memory. Alternatively, the keyword table may be generated from keyword candidates with several top-ranked importance among the keyword candidates registered in the keyword dictionary  104  and keyword candidates with several top-ranked scores among the keyword candidates stored in the memory. Here, it is preferable that the lowest rank of the keyword candidates to be registered in the keyword table can be set by the user as appropriate.  
      The key-sentence extraction unit  105  extracts each sentence, which contains any of the keywords in the keyword table set by the keyword setting unit  103  as morphemes, as a key-sentence candidate from among sentences contained in the input document and outputs it to the summary candidate setting unit  106 . Note that in this embodiment, for instance, the key-sentence candidate extraction is performed by setting a character string from a period “.” to the next period “.” as one sentence. Alternatively, a character string from a line feed mark to the next line feed mark may be set as one sentence.  
      The summary candidate setting unit  106  compares a key-sentence candidate with another key-sentence candidate inputted from the key-sentence extraction unit  105 . Following this, when the key-sentence candidate partially contains the other key-sentence candidate, the summary candidate setting unit  106  sets a character string in the matching part as a summary candidate. On the other hand, when the key-sentence candidate does not partially contain the other key-sentence candidate, the summary candidate setting unit  106  sets the key-sentence candidate as a summary candidate as it is. Note that when the number of characters of the character string in the matching part is less than the minimum number of characters M set in advance or when the number of morphemes of the character string is less than the minimum number of morphemes N set in advance, the summary candidate setting unit  106  does not set the character string in the matching part as a summary candidate but sets the key-sentence candidate as a summary candidate as it is.  
      The summary output unit  107  generates an abstract from the document information and displays it on a monitor. For instance, the summary output unit  107  displays the inputted document information in its entirety and also marks (underlines or highlights, for instance) each character string matching a summary candidate set by the summary candidate setting unit  106 . Alternatively, a format for summary may be prepared separately and each character string matching a summary candidate may be moved to the format.  
       FIG. 2  shows a processing flow of the abstract creation apparatus in this embodiment.  
      First, in step S 101 , the sentence input unit  101  receives input of document information. Next, in step S 102 , the morphological analysis unit  102  subjects the inputted document information to morphological analysis. Then, in step S 103 , the keyword setting unit  103  counts the frequency of each independent word and sets a score for the independent word in accordance with the frequency. Following this, in step S 104 , the keyword setting unit  103  generates a keyword table from each independent word (keyword candidate) having a score that is equal to or more than a threshold value K and each independent word (keyword candidate) registered in the keyword dictionary  104 . Then, in step S 105 , the key-sentence extraction unit  105  extracts each sentence, which contains any of the keywords in the generated keyword table as morphemes, as a key-sentence candidate.  
      After key-sentence candidates are extracted from the input document in this manner, next, in steps S 106  to S 111 , the summary candidate setting unit  106  carries out summary candidate setting processing described above. In more detail, first, in step S 106 , the summary candidate setting unit  106  compares a key-sentence candidate that is a judgment target with another key-sentence candidate and judges whether the key-sentence candidate partially contains (partially matches) the other key-sentence candidate. Next, when a partial matching result is not obtained, the processing proceeds to step S 109 , in which the summary candidate setting unit  106  sets the key-sentence candidate that is the judgment target as a summary candidate as it is.  
      On the other hand, when a partially matching result is obtained, the processing proceeds to step S 107 , in which the summary candidate setting unit  106  judges whether the number of characters of a character string in the partially matching part is less than a set value M. Following this, when the number of characters is less than the set value M, the processing proceeds to step S 109 , in which the summary candidate setting unit  106  sets the key-sentence candidate that is the judgment target as a summary candidate as it is. On the other hand, when the number of characters is equal to or more than the set value M, the processing proceeds to step S 108 , in which the summary candidate setting unit  106  next judges whether the number of morphemes of the character string in the partially matching part is less than a set value N. Next, when the number of morphemes is less than the set value N, the processing proceeds to step S 109 , in which the summary candidate setting unit  106  sets the key-sentence candidate that is the judgment target as a summary candidate as it is. On the other hand, when the number of morphemes is equal to or more than N, the processing proceeds to step S 110 , in which the summary candidate setting unit  106  sets the partially matching character string as a summary candidate.  
      Then, in step S 111 , the summary candidate setting unit  106  judges whether it has performed the summary candidate setting processing for every key-sentence candidate. Following this, when the summary candidate setting processing has not yet been performed for every key-sentence candidate, the summary candidate setting unit  106  repeats the operations in steps S 106  to S 110  described above. On the other hand, when the summary candidate setting processing has been performed for every key-sentence candidate, the processing proceeds to step S 112 , in which the summary output unit  107  performs summary output processing based on summary candidates. For instance, the summary output unit  107  displays the inputted document information in its entirety and also marks (underlines or highlights, for instance) each character string matching a summary candidate set in steps S 106  to S 111  described above.  
       FIGS. 3A  to  3 D show a concrete processing example at the time of the summary candidate setting.  
      When document information in one unit (electronic patient chart, for instance) is inputted into the input unit, the document information is subjected to morphological analysis, as shown in  FIG. 3A . Note that in the drawings, the sign “/” indicates the delimitations of morphemes. Following this, when “re-examination”, “medication”, and “test” are set as keywords in the keyword table, only each sentence containing any of “re-examination”, “medication”, and “test” as morphemes is extracted from among sentences contained in the document and is set as a key-sentence candidate, as shown in  FIG. 3B .  
      Next, it is judged whether a part of a key-sentence candidate matches another key-sentence candidate (whether a key-sentence candidate partially matches another key-sentence candidate) and, when a matching result is obtained, the partially matching character string is set as a summary candidate. For instance, among the key-sentence candidates shown in  FIG. 3B , “Re-examination is needed in a month” partially matches “Re-examination is needed”, as shown in  FIG. 3D . Consequently, “Re-examination is needed” is set as a summary candidate.  
      On the other hand, when a partially matching result is not obtained, the key-sentence candidate is set as a summary candidate as it is. For instance, among the key-sentence candidates shown in  FIG. 3B , “Blood test is normal” overlaps “Blood pressure test is normal” in a part “test is normal”, however, this sentence does not contain the whole of “Blood pressure test is normal” as its part, so a partially matching result is not obtained. Consequently, as shown in  FIG. 3C , “Blood test is normal” is set as a summary candidate as it is. The same applies to “Blood pressure test is normal”.  
      As described above, in this embodiment, among sentences containing keywords (key-sentence candidates), each sentence including a clause expressing a date or a period like “in a month”, a conjunction, or the like is simplified into a sentence, from which the clause, conjunction, or the like has been removed, and is set as a summary candidate. As a result, it becomes possible to generate and output an abstract where there exists no unnecessary expression such as a date, a period, or a clause.  
      Also, although not illustrated in  FIGS. 3A  to  3 D, when the number of characters in a partially matching part is less than the minimum number of characters M or when the number of morphemes in the partially matching part is less than the minimum number of morphemes N, processing is performed, in which the partially matching character string is not set as a summary candidate but the key-sentence candidate is set as a summary candidate. As a result, it becomes possible to prevent a situation where the key-sentence candidate is excessively simplified, which makes it possible to generate and output an abstract (summary) that has been simplified by an appropriate degree and gives information sufficient for contents grasping.  
      It should be noted here that the minimum number of characters M and the minimum number of morphemes N are, for instance, set by a designer at a design stage by performing summary generation on a trial basis while changing these numbers M and N as values with which it is possible to output the most effective summary. Alternatively, these values may be set so as to be settable by a user as appropriate.  
     Second Embodiment  
      In the first embodiment described above, after key-sentence candidates are extracted based on keywords, these key-sentences are simplified and are set as summary candidates. In a second embodiment, sentences contained in an input document are first simplified and then simplified sentences containing keywords are extracted and are set as summary candidates.  
       FIG. 4  shows a construction of a summary generation apparatus according to the second embodiment.  
      In  FIG. 4 , the functions of a sentence input unit  101 , a morphological analysis unit  102 , a keyword setting unit  103 , a keyword dictionary  104 , and a summary output unit  107  are the same as those shown in  FIG. 1  described above. In this embodiment, in place of the key-sentence extraction unit  105  and the summary candidate setting unit  106  in the first embodiment described above, a simplified sentence extraction unit  110  and a summary candidate setting unit  111  are used.  
      The simplified sentence extraction unit  110  compares a sentence with another sentence among sentences contained in an input document. Following this, when the sentence partially matches the other sentence, the simplified sentence extraction unit  110  sets a character string in the matching part as a simplified sentence candidate. On the other hand, when the sentence does not partially match the other sentence, the simplified sentence extraction unit  110  sets the sentence as a simplified sentence candidate as it is. However, when the number of characters of the character string in the matching part is less than the minimum number of characters M set in advance or when the number of the morphemes of the character string is less than the minimum number of morphemes N set in advance, the simplified sentence extraction unit  110  does not set the character string in the matching part as a simplified sentence candidate but sets the sentence as a simplified sentence candidate as it is.  
      The summary candidate setting unit  111  extracts each sentence containing any of keywords in a keyword table set by the keyword setting unit  103  as morphemes from among the generated simplified sentence candidates and sets the extracted sentence as a summary candidate.  
       FIG. 5  shows a processing flow of the abstract creation apparatus in this embodiment.  
      It should be noted here that in the processing flow shown in  FIG. 5 , steps S 101  to S 104  are the same as those in the processing flow shown in  FIG. 2  in the first embodiment described above, so the description thereof will be omitted.  
      In step S 104 , a keyword table is generated. Next, in step S 121 , among sentences contained in an input document, a sentence (sentence candidate) is compared with another sentence, and it is judged whether the sentence candidate partially contains (partially matches) the other sentence. Next, when a partially matching result is not obtained, the processing proceeds to step S 124 , in which the sentence candidate is set as a simplified sentence candidate as it is.  
      On the other hand, when a partially matching result is obtained, the processing proceeds to step S 122 , in which it is judged whether the number of characters of a character string in a partially matching part is less than a set value M. Next, when the number of characters is less than the set value M, the processing proceeds to step S 124 , in which the sentence candidate is set as a simplified sentence candidate as it is. On the other hand, when the number of characters is equal to or more than the set value M, the processing proceeds to step S 123 , in which it is next judged whether the number of morphemes of the character string in the partially matching part is less than a set value N. Next, when the number of morphemes is less than the set value N, the processing proceeds to step S 124 , in which the sentence candidate is set as a simplified sentence candidate as it is. On the other hand, when the number of morphemes is equal to or more than N, the processing proceeds to step S 125 , in which the partially matching character string is set as a simplified sentence candidate.  
      Then, in step S 126 , it is judged whether the simplified sentence candidate generation processing has been performed for every sentence. Following this, when the simplified sentence candidate generation processing has not yet been performed for every sentence, the operations in steps S 121  to S 125  described above are repeated. On the other hand, when the simplified sentence candidate generation processing has been performed for every sentence, the processing proceeds to step S 127 , in which each simplified sentence candidate containing any of the keywords in the keyword table generated in step S 104  as morphemes is extracted from among simplified sentence candidates and is set as a summary candidate. Then, in step S 128 , the summary output unit  107  performs abstract output processing based on each set summary candidate. For instance, the summary output unit  107  displays the inputted document information in its entirety and also marks (underlines or highlights, for instance) each character string matching a summary candidate set in steps S 121  to S 127  described above.  
       FIGS. 6A  to  6 D show a concrete processing example at the time of the summary candidate setting.  
      When document information in one unit (electronic patient chart, for instance) is inputted into the input unit, the inputted document information is subjected to morphological analysis, as shown in  FIG. 6A . After the morphological analysis, it is judged whether a part of a sentence matches another sentence (whether a sentence partially matches another sentence). Following this, when a matching result is obtained, the partially matching character string is set as a simplified sentence candidate. On the other hand, when a matching result is not obtained, the sentence is set as a simplification candidate as it is.  
      For instance, among the sentences shown in  FIG. 6A , “Re-examination is needed in a month” partially matches “Re-examination is needed”. Consequently, “Re-examination is needed” is set as a simplified sentence candidate.  
      It should be noted here that among the sentences shown in  FIG. 6A , “Blood test is normal” and “Blood pressure test is normal” partially match “normal”, however, the number of characters in the partially matching part is less than the minimum value M (M=10, for instance), so simplified sentence candidates of “Blood test is normal” and “Blood pressure test is normal” will never be set as “normal”, as shown in  FIG. 6D . Consequently, “Blood test is normal”, “Blood pressure test is normal”, and “normal” are each set as a simplification candidate as it is.  
      Next, each simplification candidate containing any of the keywords is extracted from among the generated simplification candidates and is set as a summary candidate. For instance, when “re-examination”, “medication”, and “test” are set as keywords in the keyword table, only each simplification candidate containing any of “re-examination”, “medication”, and “test” as morphemes is extracted from among the simplification candidates shown in  FIG. 6B  and is set as a summary candidate, as shown in  FIG. 6C .  
      As described above, in this embodiment, like in the first embodiment described above, it becomes possible to generate and output an abstract where there exists no unnecessary expression such as a date, a period, or a conjunction. Also, by setting the minimum number of characters M and the minimum number of morphemes N, it becomes possible to prevent excess simplification, which makes it possible to generate and output an effectively simplified abstract.  
     Third Embodiment  
      In the first embodiment described above, key-sentence candidates are extracted by comparing morphemes obtained through morphological analysis of document information with keywords (see  FIG. 3B ) and summary candidates are further extracted by comparing morphemes contained in the extracted key-sentence candidates between the key-sentences (see  FIG. 3C ). In contrast to this, in a third embodiment, the original forms of morphemes in document information are simultaneously obtained together with the morphemes (see  FIG. 7A ), and key-sentence candidates are extracted by comparing the morphemes and their original forms with keywords (see  FIG. 7B ). Then, summary candidates are extracted by comparing the morphemes contained in the extracted key-sentence candidates and their original forms between the key-sentence candidates (see  FIG. 7C ). In  FIGS. 7A  to  7 C, the original forms of morphemes are indicated with brackets.  
      In this embodiment, the function of each block of the abstract creation apparatus shown in  FIG. 1  is changed as follows.  
      The morphological analysis unit  102  includes a table, in which the original form and changed forms of each word are associated with each other, in addition to a database for morphological analysis. Like in the first embodiment described above, the morphological analysis unit  102  divides document information in one unit inputted from the input unit  101  into morphemes and gives punctuation information and information showing whether the morphemes are each an independent word or an adjunct to the document information. When doing so, at the same time, each morpheme is given information concerning its original form while referring to the table described above.  
      The keyword setting unit  103  detects the occurrence frequency of the original form of each independent word contained in the document information and stores the original form of each independent word, whose occurrence frequency is equal to or more than a predetermined threshold value, as a keyword candidate in a memory (not shown). When doing so, for the keyword candidate, a score corresponding to the occurrence frequency is set and is stored in the memory.  
      The keyword setting unit  103  generates a keyword table from the keyword candidates (original forms of independent words) stored in the memory and keyword candidates registered in the keyword dictionary  104 . This keyword table is referred to at the time of key-sentence extraction by the key-sentence extraction unit  105 . Like in the first embodiment described above, the keyword table is, for instance, generated from every keyword candidate registered in the keyword dictionary  104  and keyword candidates with several top-ranked scores among the keyword candidates (original forms of independent words) stored in the memory.  
      The key-sentence extraction unit  105  extracts each sentence, which contains any of the keywords in the keyword table set by the keyword setting unit  103  as morphemes or their original forms, as a key-sentence candidate from among sentences contained in an input document. Then, the key-sentence extraction unit  105  outputs the morphemes contained in the key-sentence candidate and their original forms to the summary candidate setting unit  106 .  
      The summary candidate setting unit  106  compares a key-sentence candidate with another key-sentence candidate inputted from the key-sentence extraction unit  105  and judges whether the key-sentence candidate partially contains the other key-sentence candidate. This judgment is made by comparing the two target key-sentence candidates as to morphemes and their original forms. Next, when judging that the key-sentence candidate that is a judgment target partially contains the other key-sentence candidate in terms of morphemes or their original forms, the summary candidate setting unit  106  sets the original forms of a character string in the matching part as a summary candidate. On the other hand, when the key-sentence candidate that is the judgment target does not partially contain the other key-sentence candidate in terms of morphemes or their original forms, the summary candidate setting unit  106  sets the original forms of morphemes contained in the key-sentence candidate as a summary candidate.  
      However, like in the first embodiment described above, when the number of characters of the character string in the matching part is less than the minimum number of characters M set in advance or when the number of morphemes of the character string is less than the minimum number of morphemes N set in advance, the summary candidate setting unit  106  does not set the character string in the matching part as a summary candidate but sets the original forms of the morphemes contained in the key-sentence candidate as a summary candidate.  
      The summary output unit  107  generates an abstract from the document information and displays it on a monitor. For instance, the summary output unit  107  displays the inputted document information in its entirety and also marks (underlines or highlights, for instance) each character string whose original forms match a summary candidate (original forms of morphemes) set by the summary candidate setting unit  106 . Aside from this form, a format for summary may be prepared separately, and each character string, whose original forms match a summary candidate, may be moved to the format.  
       FIG. 8  shows a processing flow of the abstract creation apparatus in this embodiment.  
      In step S 201 , the sentence input unit  101  receives input of document information. Then, in step S 202 , the morphological analysis unit  102  subjects the inputted document information to morphological analysis and also adds the original form of each morpheme to the document information. Then, in step S 203 , the keyword setting unit  103  counts the frequency of the original form of each independent word and sets a score corresponding to the frequency for the original form of the independent word. Next, in step S 204 , the keyword setting unit  103  generates the keyword table from the original form (keyword candidate) of each independent word having a score that is equal to or more than a threshold value K and the independent words (keyword candidates) registered in the keyword dictionary  104 . Then, in step S 205 , the key-sentence extraction unit  105  extracts each sentence containing any of the keywords in the generated keyword table as morphemes or their original forms as a key-sentence candidate.  
      After key-sentence candidates are extracted from the input document in this manner, next, in steps S 206  to S 211 , the summary candidate setting unit  106  carries out summary candidate setting processing described above. In more detail, first, in step S 206 , the summary candidate setting unit  106  compares a key-sentence candidate that is a judgment target with another key-sentence candidate and judges whether the key-sentence candidate partially contains (partially matches) the other key-sentence candidate in terms of morpheme or its original form. Next, when a partial matching result is not obtained, the processing proceeds to step S 209 , in which the summary candidate setting unit  106  sets the original form of the morpheme contained in the key-sentence candidate that is the judgment target as a summary candidate as it is.  
      On the other hand, when a partially matching result is obtained, the processing proceeds to step S 207 , in which the summary candidate setting unit  106  judges whether the number of characters of a character string in the partially matching part is less than a set value M. Following this, when the number of characters is less than the set value M, the processing proceeds to step S 209 , in which the summary candidate setting unit  106  sets the original form of the morpheme contained in the key-sentence candidate that is the judgment target as a summary candidate. On the other hand, when the number of characters is equal to or more than the set value M, the processing proceeds to step S 208 , in which the summary candidate setting unit  106  next judges whether the number of morphemes of the character string in the partially matching part is less than a set value N. Next, when the number of morphemes is less than the set value N, the processing proceeds to step S 209 , in which the summary candidate setting unit  106  sets the original form of the morpheme contained in the key-sentence candidate that is the judgment target as a summary candidate as it is. On the other hand, when the number of morphemes is equal to or more than N, the processing proceeds to step S 210 , in which the summary candidate setting unit  106  sets the original form of the partially matching character string as a summary candidate.  
      Then, in step S 211 , the summary candidate setting unit  106  judges whether it has performed the summary candidate setting processing for every key-sentence candidate. Following this, when the summary candidate setting processing has not yet been performed for every key-sentence candidate, the summary candidate setting unit  106  repeats the operations in steps S 206  to S 210  described above. On the other hand, when the summary candidate setting processing has been performed for every key-sentence candidate, the processing proceeds to step S 212 , in which the summary output unit  107  performs summary output processing based on summary candidates. For instance, the summary output unit  107  displays the inputted document information in its entirety and also marks (underlines or highlights, for instance) each character string, which original form matches a summary candidate set in steps S 206  to S 211  described above.  
      According to this embodiment, each key-sentence candidate is extracted by comparing morphemes in document information and their original forms with keywords. As a result, even when morphemes in forms, in which the keywords have been changed from their original forms, are contained in the document information, it becomes possible to extract each sentence containing any of the morphemes that are in the changed forms of keywords as a key-sentence candidate. Note that in the above description, the keyword candidates registered in the keyword dictionary  104  are registered in the keyword table as they are, however instead of this form, the original forms of the keyword candidates may be registered in the keyword table. With this construction, it becomes possible to include each sentence, which a user wishes to insert in a summary, as a key-sentence candidate with more reliability.  
      Also, according to this embodiment, each summary candidate is extracted by comparing morphemes in document information and their original forms between key-sentence candidates. As a result, even when morphemes contained in the key-sentence candidates have been changed from their original forms (for instance, a lowercase letter has been changed to an uppercase letter or a singular form has been changed to a plural form), it becomes possible to make a precise judgment as to matching between the key-sentence candidates. As a result, it becomes possible to perform the simplification of the key-sentence candidates more smoothly.  
     Fourth Embodiment  
      In the second embodiment described above, simplified sentence candidates are extracted by comparing morphemes obtained through morphological analysis of document information between sentences (see  FIG. 6B ), and summary candidates are further extracted by comparing the morphemes contained in the extracted simplified sentence candidates with keywords (see  FIG. 6C ). In contrast to this, in a fourth embodiment, the original forms of morphemes of document information are simultaneously obtained together with the morphemes (see  FIG. 9A ), and simplified sentence candidates are extracted by comparing the morphemes and their original forms between sentences (see  FIG. 9B ). Then, summary candidates are extracted by comparing morphemes contained in the extracted simplified sentence candidates and their original forms with keywords (see  FIG. 9C ). In  FIGS. 9A  to  9 C, the original forms of morphemes are indicated with brackets.  
      In this embodiment, the function of each block of the abstract creation apparatus shown in  FIG. 4  is changed as follows.  
      The functions of the morphological analysis unit  102  and the keyword setting unit  103  are changed in the same manner as in the case of the third embodiment described above. Note that the functions of the document input unit  101  and the keyword dictionary  104  are the same as those in the case of the second embodiment described above.  
      The simplified sentence extraction unit  110  compares a sentence with another sentence among sentences contained in an input document. Then, when the sentence partially matches the other sentence in terms of morphemes or their original forms, the simplified sentence extraction unit  110  sets a character string in the matching part and its original forms as a simplified sentence candidate. On the other hand, when a partially matching result is not obtained, the simplified sentence extraction unit  110  sets morphemes contained in the sentence and their original forms as a simplified sentence candidate. However, when the number of characters of the character string in the matching part is less than the minimum number of characters M set in advance or when the number of morphemes of the character string is less than the minimum number of morphemes N set in advance, the simplified sentence extraction unit  110  does not set the character string in the matching part as a simplified sentence candidate but sets the morphemes contained in the sentence and their original forms as a simplified sentence candidate.  
      The summary candidate setting unit  111  extracts each simplified sentence candidate containing any of the keywords in the keyword table set by the keyword setting unit  103  as morphemes or their original forms from among generated simplified sentence candidates and sets the original forms of the extracted simplified sentence candidate as a summary candidate.  
       FIG. 10  shows a processing flow of the abstract creation apparatus in this embodiment.  
      It should be noted here that in the processing flow shown in  FIG. 10 , steps S 201  to S 204  are the same as those in the processing flow shown in  FIG. 8  in the third embodiment described above, so the description thereof will be omitted.  
      In step S 204 , a keyword table is generated. Next, in step S 221 , among sentences contained in an input document, a sentence (sentence candidate) is compared with another sentence and it is judged whether the sentence candidate partially contains (partially matches) the other sentence in terms of morphemes or their original forms. Next, when a partially matching result is not obtained, the processing proceeds to step S 224 , in which each morpheme contained in the sentence candidate and its original form are set as a simplified sentence candidate.  
      On the other hand, when a partially matching result is obtained, the processing proceeds to step S 222 , in which it is judged whether the number of characters of a character string in the partially matching part is less than a set value M. Next, when the number of characters is less than the set value M, the processing proceeds to step S 224 , in which each morpheme contained in the sentence candidate and its original form are set as a simplified sentence candidate. On the other hand, when the number of characters is equal to or more than the set value M, the processing proceeds to step S 223 , in which it is next judged whether the number of morphemes of the character string in the partially matching part is less than a set value N. Next, when the number of morphemes is less than the set value N, the processing proceeds to step S 224 , in which each morpheme contained in the sentence candidate and its original form are set as a simplified sentence candidate. On the other hand, when the number of morphemes is equal to or more than N, the processing proceeds to step S 225 , in which the partially matching character string and its original forms are set as a simplified sentence candidate.  
      Then, in step S 226 , it is judged whether the simplified sentence candidate generation processing has been performed for every sentence. Following this, when the simplified sentence candidate generation processing has not yet been performed for every sentence, the operations in steps S 221  to S 225  described above are repeated. On the other hand, when the simplified sentence candidate generation processing has been performed for every sentence, the processing proceeds to step S 227 , in which each simplified sentence candidate containing any of the keywords in the keyword table generated in step S 204  as morphemes or their original forms is extracted from among simplified sentence candidates and the original forms of the extracted simplified sentence candidate are set as a summary candidate. Then, in step S 228 , the summary output unit  107  performs abstract output processing based on each set summary candidate. For instance, the summary output unit  107  displays the inputted document information in its entirety and also marks (underlines or highlights, for instance) each character string whose original forms match a summary candidate set in steps S 221  to S 227  described above.  
      According to this embodiment, each simplified sentence candidate is extracted by comparing morphemes in document information and their original forms between sentences. As a result, even when morphemes contained in the sentences have been changed from their original forms (for instance, a lowercase letter has been changed to an uppercase letter or a singular form has been changed to a plural form), it becomes possible to make a precise judgment as to matching between the sentences. As a result, it becomes possible to perform the simplification of the sentences more smoothly.  
      Also, according to this embodiment, each summary candidate is extracted by comparing morphemes in simplified sentence candidates and their original forms with the keywords. As a result, even when morphemes in forms, in which the keywords have been changed from their original forms, are contained in the simplified sentence candidates, it becomes possible to extract each simplified sentence candidate containing any of the morphemes that are in the changed forms of keywords as a summary candidate. Note that in the above description, the keyword candidates registered in the keyword dictionary  104  are registered in the keyword table as they are, although instead of this form, the original forms of the keyword candidates may be registered in the keyword table. With this construction, it becomes possible to extract each sentence, which a user wishes to insert in a summary, as a key-sentence candidate with more reliability.  
      The present invention is not limited to the embodiments described above and it is possible to make various changes. For instance, in each embodiment described above, the morphemes are set as words, although the morphological analysis may be performed by setting the morphemes as word groups, such as “blood pressure” and “after all”, that each give a certain meaning through a combination of several words. It is possible to change the embodiments of the present invention as appropriate without departing from the scope of the technical idea described in the appended claims.