Methods and systems for generating textual information

• US 7,310,633 B1
• Filed: 03/31/2004
• Issued: 12/18/2007
• Est. Priority Date: 03/31/2004
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Wang, Niniane, Lawrence, Stephen R.
• Primary Examiner(s): Rimell, Sam
• Assistant Examiner(s): Lewis, Alicia M
• Application Number:
  US10/814,487
• Time in Patent Office: 1,357 Days
• Field of Search: 707/1, 707/3, 707/4, 707/100
• US Class Current: 707/742
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/345 Summarisation for human users
• CPC Class Codes: Y10S 707/918 Location
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching

Abstract

Methods and systems for generating textual information are disclosed. In one exemplary embodiment, a method of generating textual information is disclosed that comprises identifying a plurality of candidate summaries related to textual information based at least in part on a document, determining first and second attribute values based at least in part on the candidate summaries, and determining an optimal candidate summary based at least in part on the first and second attribute values.

Claims

1. A method comprising:

identifying a plurality of candidate summaries related to textual information based at least in part on a document;

determining first and second attribute values based at least in part on the candidate summaries;

determining a number of storage locations for the plurality of candidate summaries, the number of storage locations based at least in part on a size of the document;

combining the plurality of candidate summaries into a plurality of combined candidate summaries;

selecting from the plurality of combined candidate summaries a first highest-weighted combined candidate summary and a second highest-weighted combined candidate summary;

comparing the first and second highest-weighted combined candidates; and

determining an optimal candidate summary based at least in part on the first and second attribute values and at least in part on the comparison of the first and second highest-weighted combined candidate summaries; and

outputting the optimal candidate summary.

2. The method of claim 1 further comprising tokenizing the document.

3. The method of claim 1, wherein the size of the document comprises a number of words disposed in the document.

4. The method of claim 1, wherein the size of the document comprises a number of character strings disposed in the document.

5. The method of claim 1, wherein the number of storage locations is based at least in part on at least one of the size of the document, a desired size of the summary, and a number of query terms.

6. The method of claim 2 further comprising tokenizing a portion of the document sufficient to generate a predetermined number of candidate summaries.

7. The method of claim 1, wherein the first and second attribute values comprise a number of words.

8. The method of claim 1, wherein the first and second attribute values comprise a pixel size.

9. The method of claim 1, wherein the first and second attribute values comprise a string length.

10. The method of claim 1, wherein a number of combined candidates is less than or equal to the number of storage locations.

11. The method of claim 1, wherein each of the storage locations comprises a bucket and wherein the first and second attribute values are mapped onto the bucket.

12. The method of claim 1, wherein the candidate summaries comprise at least one keyword.

13. The method of claim 12, wherein the keyword comprises a first keyword and a second keyword, the optimal candidate summary further determined based at least in part on a title of the document comprising the first keyword and one of the plurality of candidate summaries comprising the second keyword.

14. The method of claim 12, wherein the first and second highest-weighted combined candidates are selected based at least in part on a percentage of keywords included in the plurality of candidate summaries.

15. The method of claim 12, wherein the document comprises a plurality of sections and wherein the first and second highest-weighted combined candidates are selected based at least in part on multiple occurrences of the keyword in one of the plurality of sections.

16. The method of claim 12 further comprising adjusting a weighting of the first and second highest-weighted combined candidate summaries based at least in part on a number of the keywords spanned by the plurality of candidate summaries.

17. A method comprising:

searching a document;

identifying a keyword disposed in the document;

identifying a plurality of candidate summaries related to textual information based at least in part on the document;

determining a number of storage locations for the plurality of candidate summaries, the number of storage locations based at least in part on a size of the document;

combining the plurality of candidate summaries into a plurality of combined candidate summaries;

determining first and second attribute values based at least in part on the candidate summaries;

selecting from the plurality of combined candidate summaries a first highest-weighted combined candidate summary and a second highest-weighted combined candidate summary;

determining an optimal candidate summary based at least in part on the first and second attribute values; and

comparing the first and second highest-weighted combined candidate summaries, wherein determining the optimal candidate summary is further based at least in part on the comparison of the first and second highest-weighted combined candidate summaries; and

outputting a summary result including the optimal candidate summary.

18. A computer readable storage medium comprising instructions, that, when executed, cause an application to:

identify a plurality of candidate summaries related to textual information based at least in part on a document;

determine first and second attribute values based at least in part on the candidate summaries;

determine a number of storage locations for the plurality of candidate summaries, the number of storage locations based at least in part on a size of the document;

combine the plurality of candidate summaries into a plurality of combined candidate summaries;

select from the plurality of combined candidate summaries a first highest-weighted combined candidate summary and a second highest-weighted combined candidate summary;

compare the first and second highest-weighted combined candidate summaries, and

determine an optimal candidate summary based at least in part on the first and second attribute values and at least in part on the comparison of the first and second highest-weighted combined candidate summaries.

19. The computer readable storage medium of claim 18, further comprising instructions, that, when executed, cause an application to tokenize the document.

20. The computer readable storage medium of claim 18, wherein the size of the document comprises a number of words disposed in the document.

21. The computer readable storage medium of claim 18, wherein the size of the document comprises a number of character strings disposed in the document.

22. The computer readable storage medium of claim 18, wherein the number of storage locations is based at least in part on at least one of the size of the document, a desired size of the summary, and a number of query terms.

23. The computer readable storage medium of claim 18, wherein the first and second attribute values comprise a number of words.

24. The computer readable storage medium of claim 18, wherein the first and second attribute values comprise a pixel size.

25. The computer readable storage medium of claim 18, wherein the candidate summaries comprise a string length.

26. The computer readable storage medium of claim 18, wherein a number of combined candidate summaries is less than or equal to the number of storage locations.

27. The computer readable storage medium of claim 18, wherein each of the storage locations comprises a bucket and wherein the first and second attribute values are mapped onto the bucket.

28. The computer readable storage medium of claim 18, wherein the candidate summaries comprise at least one keyword.

29. The computer readable storage medium of claim 28, wherein the keyword comprises a first keyword and a second keyword, the optimal candidate summary determined based at least in part on a title of the document comprising the first keyword and one of the plurality of candidate summaries comprising the second keyword.

30. The computer readable storage medium of claim 28, wherein the first and second highest-weighted combined candidates are selected based at least in part on a percentage of the keywords included in the plurality of candidate summaries.

31. The computer readable storage medium of claim 28, wherein the document comprises a plurality of sections and wherein the first and second highest-weighted combined candidates are selected based at least in part on multiple occurrences of the keyword in one of the plurality of sections.

32. The computer readable storage medium of claim 19 further comprising instructions, that, when executed, cause an application to tokenize a portion of the document sufficient to generate a predetermined number of candidate summaries.

33. The computer readable storage medium of claim 28 further comprising instructions, that when executed cause an application to adjust a weighting of the first and second highest-weighted combined candidate summaries based at least in part on a number of the keywords spanned by the plurality of candidate summaries.