11institutetext: School of Information and Library Science
University of North Carolina at Chapel Hill
11email<EMAIL_ADDRESS>
https://aeshin.org

# Automatically
Segmenting Oral History Transcripts

Ryan Shaw

###### Abstract

Dividing oral histories into topically coherent segments can make them more
accessible online. People regularly make judgments about where coherent
segments can be extracted from oral histories. But making these judgments can
be taxing, so automated assistance is potentially attractive to speed the task
of extracting segments from open-ended interviews. When different people are
asked to extract coherent segments from the same oral histories, they often do
not agree about precisely where such segments begin and end. This low
agreement makes the evaluation of algorithmic segmenters challenging, but
there is reason to believe that for segmenting oral history transcripts, some
approaches are more promising than others. The BayesSeg algorithm performs
slightly better than TextTiling, while TextTiling does not perform
significantly better than a uniform segmentation. BayesSeg might be used to
suggest boundaries to someone segmenting oral histories, but this segmentation
task needs to be better defined.

###### Keywords:

oral history, discourse segmentation, natural language processing, digital
libraries

##  1 Introduction

Oral histories are rich and unique documents of the past and our memory of it.
Putting oral histories on the web makes them more accessible, but they remain
daunting to consume [9]. It requires a significant time commitment to listen
to a one or two hour interview. This is why curators of oral history
collections, when creating “exhibits” of their materials for the public,
usually select short extracts from longer interviews. Scholars also select
extracts from their recordings when presenting their work to a live audience
[19, 265]. Short, self-contained extracts of oral histories are more
approachable than the longer recordings from which they are taken, but
selecting these extracts is labor-intensive.

The labor required to select extracts from long interviews could be reduced
through partial automation. The selection of extracts can be modeled as a two-
step process: first separate the interview into segments, then select the
segments of interest. In this paper I focus on the first step: segmentation of
interview transcripts. Automatic segmentation of discourse is an established
problem for natural language processing. Research has shown that the efficacy
of segmentation algorithms varies widely with text genre and the kinds of
segments desired [18, 38]. Thus one cannot assume that segmentation techniques
that have proved effective for other genres of discourse, using different
definitions of segment, will work well for finding potential extract
boundaries in oral history interviews.

I collected 829 judgments about oral history boundaries. I then used their
judgments to evaluate the performance of two automatic text segmentation
algorithms, TextTiling [11] and BayesSeg [4]. The results showed that BayesSeg
performs slightly better than TextTiling, while TextTiling does not perform
significantly better than a uniform segmentation (all segments of equal
length). On the basis of these results, I conclude that BayesSeg might be used
to lower the costs of selecting extracts from oral histories, but that the
segmentation task for oral histories needs to be better defined.

##  2 Choosing the Telling Extract

Oral historians see “choosing the telling extract” [19, 265] as an essential
part of communicating to their audiences. The actual words of interviewees are
more evocative that the historian’s paraphrase, and extracts are seen as
essential to providing “texture” and “voice” to the historian’s interpretation
[15, 27–28]. While some historians will limit themselves to pulling brief
quotes to illustrate their arguments, others yield far more to their
respondents, aiming to let them “tell their own story” by stitching together
long extracts. This is particularly common when the medium of communication
allows for audio to be presented. When giving talks, oral historians will
often build their presentation around a handful of extracts, four or five
minutes each [19, 266]. Incorporating such extracts is now a possibility for
authors of monographs as well, thanks to recent developments in e-books.

Libraries and archives have made oral history interviews considerably more
accessible by digitizing and publishing them on the web. A web-based finding
aid typically lists the interviews available with descriptions of who was
interviewed, when and where, and a set of subject headings indicating the
content of the interview. Yet as Frisch [9] and others have argued, this kind
of description at the interview level is by itself insufficient for making the
content of oral histories truly accessible to the public. Oral history
interviews are often quite long. Given only a summary description of an
interview’s content, a listener must either dedicate an hour or two to
listening to an interview in its entirety or use audio player controls to
tediously jump around in a file looking for parts of interest. Furthermore,
interviews varying in length typically are given descriptions of equivalent
extent, meaning that long interviews are less “densely” described. Interviews
can be made more accessible to the public by dividing them into shorter
segments that can be consumed more easily and described more comprehensively.
Lambert and Frisch [14] advocate for segments of five to fifteen minutes in
length, with boundaries at “a natural ending of a topic, a break before a new
question, or other natural pause in the flow of speech.” Making such segments,
rather than whole interviews, the target of archival description “unifies the
scale of the navigable unit” [14] and solves the problem of longer interviews
being less comprehensively described than shorter ones.

Oral histories have a recognizable structure: a sequence of speaking turns
taken by the interviewer and respondent. The possibility of identifying
topical segments within this structure is implicit in the extractions oral
historians make from their interviews when presenting their work to others. In
some cases, these topical segments may directly correspond to speaking turns,
as when an interviewer asks a topical question and the respondent answers it
completely and without digression in a single turn. But usually speaking turns
do not map neatly to topics: topics either stretch across several turns, or a
single long response encompasses more than one topic. Interviewers may
introduce topics, but respondents have their own agendas and may or may not
follow where the interviewer leads. Despite these complications, historians
are able to make topical segmentation decisions. If these decisions are
grounded in evidence given in the interviews themselves (e.g. the language
used), then one might expect there to be a certain degree of consensus among
the decisions made by different individuals.

##  3 Discourse Segmentation

In the previous section I characterized a topical segment in a pragmatic way
as a salient, coherent extract from an oral history. But the more general
notion of a topical segment is harder to characterize, as topic is not a
clearly defined concept [18, 17]. Intuitively, a topic is something that some
passage of text is “about,” but given a passage that is judged to be about a
topic $A$, one can often discern within that passage subtopics $A_{1}$,
$A_{2}$, and so on—right on down to the level of individual utterances.
Topical structure is hierarchical [17]. Ashplant [1, 107] demonstrates that
this is true of oral histories in his analysis of part of The Dillen [12]. He
discerns a three-level topical hierarchy, with anecdotes grouped into
narrative elements, which are in turn grouped into broad topical clusters.
Unfortunately, hierarchies can be computationally intractable, so it is
preferable to focus on a specific level in the topical hierarchy that is
appropriate for the task at hand, making the topical segmentation a flat, non-
overlapping partitioning of the text [18, 17]. For the specific task of
selecting extracts from oral histories, I focus on segments in the middle of
the topical hierarchy, equivalent to Ashplant’s narrative elements.

The vast majority of work on processing transcripts to segment recordings of
speech has focused on broadcast news (e.g. [2]) or conversational dialogue in
settings such as phone conversations and meetings (e.g. [10]). But few studies
have focused specifically on segmentation for the purpose of identifying
potential extracts from oral histories. Franz et al. [8], working with a
collection of oral history interviews with Holocaust survivors, trained a
classifier for recognizing segment boundaries, using as training data the
segment boundaries defined by catalogers. Using a training set of
approximately 1.5 million words (about 2800 segments), they achieved an equal
error rate of 23% on a manually transcribed test set. In a follow-up study
using a subset of the same corpus, Zhang and Soergel [21] did a qualitative
coding of questions and cataloging segments, and explored using these codes as
features in a classifier for recognizing topical segment boundaries, with
inconclusive results.

A drawback of treating topic segmentation as a supervised classification
problem is that it requires as training data a corpus of texts that have been
manually annotated with topic boundaries. Generating such data is expensive.
This expense is increased by the fact that judgments about topic change are
largely made intuitively, resulting in low inter-annotator agreement, so
finding a consensus on boundary judgments may require several annotators for
the same text [18, 18]. Thus most research has focused on finding unsupervised
techniques for segmenting topics.

The assumption behind unsupervised topic segmentation of a continuous text is
that there will be greater similarity in the use of language within topically
coherent segments than across such segments. So the first question to be
answered is how “similarity in the use of language” will be defined and
quantitatively measured. Once that is answered, a continuous text can be
broken into pieces (such as individual sentences) and the similarity of those
pieces to one another can be measured. This will produce an overall map of the
similarity of sub-segment units in the text. The next step is to use this map
to group these units into larger topical segments. This requires answering a
second question: given some measure of similarity among the analyzed units,
how should one decide whether a topical boundary separates them? Thus any
technique for topically segmenting continuous text can be characterized in
terms of how it addresses two sub-problems: measuring language similarity and
postulating boundaries [18, 30].

A straightforward way of measuring language similarity is to directly compare
the words used. The TextTiling algorithm [11] treats regular chunks of text as
term vectors and measures the cosine similarity between them. Hearst
demonstrated that it worked well for segmenting the expository prose found in
a popular science article. An alternative to directly comparing word
distributions is to compare higher-level semantic or statistical
representations of the text, so that different but related words may
contribute to the similarity measure. One such representation is a
probabilistic language model, which assumes that texts are generated according
to (unknown) probability distributions over topics and words. The BayesSeg
algorithm [4] uses such a generative language model to formalize the notion of
language similarity: similar texts are ones produced by the same lexical
distribution. Intuitively, one might expect oral history respondents to use a
less consistent vocabulary than professional writers of expository texts such
as news articles, which suggests that a similarity measure based on a higher-
level representation of language may be more effective for segmenting oral
history transcripts.

Once there is a way of measuring language similarity between different parts
of a text, one can turn to the problem of postulating topic segment
boundaries. One strategy is to compare the similarity of adjacent chunks of
text, looking for “valleys” (local minima) in a plot of the similarity scores.
This is the approach taken by the TextTiling algorithm. Again, one might
expect this approach to work well for professionally-produced texts with clear
shifts in topic. But Malioutov and Barzilay [16] demonstrated that only
examining adjacent chunks performs less well when topic shifts are more
subtle. Working with transcripts of classroom lectures, they obtained better
segmentation performance by comparing (nearly) all pairs of chunks in a text,
enabling them to find longer-range similarities in language use. BayesSeg [4]
also finds a globally optimal solution, taking advantage of its probabilistic
model to find the most likely segmentation. Because topic shifts in oral
histories are gradual and nonlinear, a boundary computation approach that only
compares adjacent blocks should be outperformed by one that can take the
entire text into account.

##  4 Data and Methods

Our corpus consisted of 19 transcripts of oral history interviews conducted by
the Southern Oral History Program (SOHP) at the University of North
Carolina.111All of the data and code discussed in this paper are available at
https://github.com/contours. In an earlier project, SOHP staff transcribed and
selected salient extracts from each of the interviews, producing TEI XML files
containing the transcribed text with milestones indicating the start and end
of each extract. The milestones thus divide each interview into segments, of
which some subset (the selected extracts) have been judged to be topically
coherent. On average, half of the segments were selected as salient extracts.
The unselected segments also tended to be longer, with a mean length of 70
sentences compared to 44 sentences for the selected extracts. This suggests
that the extraction process may not identify some potential topic boundaries
(those that appear within the segments of interviews judged to be less
salient).

I then asked two non-expert annotators to imagine that they had been tasked
with curating an online collection of oral histories and to select “the most
important parts” from each transcript. Each transcript was presented as an
HTML page showing only the names of the speakers and the transcribed text of
their speech. The annotators could click on the text to split it into segments
and then indicate which segments were to be selected as extracts. They were
instructed that each extract “should cover a single topic or anecdote and
should be understandable on its own.” To give them a sense of the expected
granularity of the extracts, the annotators were told that the length of
extracts could vary considerably but would average around 30–50 sentences (the
average extract length in the original project).222The complete text of the
instructions provided to the annotators is available at
https://github.com/contours/segment/blob/5404fce/public/instructions.html.
Extracts could not overlap, and not all of the text had to be extracted (i.e.
it was permissible to “leave out” parts of the transcript between extracts).
Extract boundaries were not limited to speaker changes or paragraph breaks and
could be placed between any two adjacent sentences.

Rather than evaluating the segmentation algorithms with the best published
results, I sought to evaluate algorithms representing different solutions to
the two problems of measuring language similarity and postulating topic
boundaries. TextTiling [11], as described above, uses the cosine metric to
quantify text similarity, and a local valley-finding algorithm to postulate
boundaries. BayesSeg [4] probabilistically models similar text similarity as
the product of a sparse lexical distribution, and uses a global expectation-
maximization approach to postulate boundaries. I hypothesized that BayesSeg
would better handle the inconsistent vocabulary and gradual topic shifts found
in oral histories. To test this hypothesis, new implementations of both these
algorithms were developed and the segmentations they produced compared to the
manually-created segmentations.

Judgments about topic change are largely intuitive, which makes evaluation of
topic segmentation techniques tricky. There are two main problems. The first
problem is that it is difficult to produce a “gold standard” to evaluate
against, or even to define what such a standard might be, in the absence of
any clear rules to establish when a topic change has occurred. A common
strategy for producing a gold standard is to have several human annotators
segment the same texts and then try to determine the “consensus” segmentation
by majority opinion. A second problem is that even given an accepted
segmentation for comparison, there remains the issue of how to compare
candidate segmentations to the accepted one. Most researchers have taken the
approach of moving a window through both a gold standard segmentation and a
segmentation to be evaluated, and then counting how many times the boundary
counts postulated by the two algorithms within the window differ. This
approach has various problems, including variance in how different kinds of
errors are penalized and sensitivity to an arbitrary window size parameter
[6]. To avoid these issues I used the boundary edit distance metric proposed
by Fournier [7], which does not require any parameters and does not rely on a
gold standard segmentation. ￼

##  5 Comparing Manual Segmentations

The three annotators placed a total of 829 boundaries between sentences of the
19 interview transcripts, creating a total of 886 segments. Figure 1 shows the
distribution of segment lengths, which, like the topic segmentation datasets
examined by Fournier [6], appears to be exponential. The mean segment length
was $50\pm 52.7$ sentences ($n=886$). The shortest and longest segments
created were one sentence and 621 sentences long, respectively. The longest
segment may be an error, since it is far longer than any other segment in the
dataset and far longer than the other segments created by that annotator for
that interview. Some of the very short segments seem to be cases of “trimming”
behavior, where a short segment is created specifically to be excluded (e.g.
an interviewer unsuccessfully trying to interrupt an interviewee with a new
question) or highlighted (e.g. a particularly vivid single quote that makes
sense in isolation). Different tendencies to “trim” like this may explain some
of the variations observed across the annotators’ segmentations.

Figure 1: Lengths in sentences of the manually-created segments.

If the annotators had consistently segmented at the same level of granularity,
one would expect to see little variance in the segment lengths and a positive
correlation between each transcript’s length and the number of segments into
which it was divided. As figure 1 shows, segment lengths varied significantly.
While there was a positive correlation between transcript length and the
number of segments, there was also a positive correlation between transcript
length and the median segment length. Thus while the annotators divided longer
transcripts into more segments, they also created longer segments for longer
transcripts. Longer interview transcripts might reflect the fact that some
interviewees are more long-winded than others. If that were the case then one
might expect longer segments for longer interviews: each topic takes longer to
cover. However it is also possible that longer interview transcripts simply
cover more topics than others. If so, then the division of longer interviews
into longer segments may have been due to annotator exhaustion, resulting in
interviews segmented at different levels of granularity.

Segmentation granularity also varied across annotators. The original extractor
placed boundaries slightly more frequently than the overall rate, which was
about one boundary per 59 potential boundaries. Annotator A placed fewer
boundaries (creating longer segments) on average than either the original
extractor or annotator B. Annotator B placed more boundaries (creating shorter
segments) on average than the other two. This could indicate that annotator B
engaged in more “trimming” than the other annotators. These differences in
boundary placement rates across annotators might lead one to expect low inter-
annotator agreement, and indeed that is the case.

To measure inter-annotator agreement I used the boundary edit distance metric
proposed by Fournier [7]. This metric compares two segmentations by examining
pairs of boundary placed and treating each pair as a “match” (both
segmentations placed a boundary at the same point), a “near miss” (the two
segmentations placed boundaries close to one another but not at the same
point), or a “miss” (only one of the two segmentations placed a boundary).
Matches are given a score of one, misses a score of zero, and scores for near
misses are scaled by how close the nearly missing boundaries are. The combined
scores are normalized so that segmentation comparisons consisting of all
misses will have a boundary similarity score of zero, and segmentations
consisting of all matches will have a score of one. How near a pair of
boundaries must be to be considered a “near miss” rather than a miss is
determined by a transposition spanning distance parameter $n_{t}$: if the
boundaries are within $n_{t}$ potential boundaries of one another, they are a
near miss.

Fournier [6, 152] suggests that for comparing segmentations of texts at
paragraph boundaries, setting $n_{t}$ to 2 is appropriate, so that only pairs
of boundary judgments that differ by one paragraph (and no more) will be
considered “near misses.” Interview transcripts, being transcriptions of
spoken language, are divided not into paragraphs but into speaker turns.
However our units of segmentation are sentences, not speaker turns, as a topic
change might be judged to occur mid-turn (i.e. when a speaker changes the
subject). The mean interviewee turn length in sentences was 8, so I set
$n_{t}=9$ so that pairs of boundary judgments differing by eight sentences or
less would be treated as “near misses.” With $n_{t}=9$, the micro-averaged
edit distance between pairs of boundaries placed by two annotators was
$0.27\pm 0.0232$ (95% CI, $n=1270$). This pairwise mean boundary edit distance
measures actual agreement; to correct for chance agreement one can calculate
Fleiss’ $\pi^{\ast}$ coefficient [5], which was also 0.27. The closeness of
agreement corrected for chance and actual agreement indicates that expected
agreement due to chance is quite low. This is as expected given that were few
boundaries placed compared to the number of potential boundaries.

##  6 Comparing Automatic Segmentations

All 19 transcripts were automatically segmented using our implementations of
the TextTiling and BayesSeg algorithms. Stopwords were removed (using Choi’s
stopword list[3]) and tokens were stemmed before segmentation. Before running
the BayesSeg algorithm, the tokens were further filtered by POS-tagging them
using the Stanford POS tagger [20] and removing all but the nouns. The
TextTiling algorithm has a number of parameters to be set. I used the same
defaults as [11] (token-sequence size of 20, block size of 10, one round of
smoothing, and smoothing width of 2), and the “liberal” boundary threshold of
one standard deviation from the mean depth score. BayesSeg requires that the
number of desired segments be specified, so I set the number of desired
segments for each transcript to be equal to the number of segments created for
that transcript by the original extractor.

I evaluated the two automatic segmentation algorithms using the approach
advocated by Fournier [6]. The segmentations produced by each algorithm were
compared to all of the manual segmentations. Because boundary edit distance
can be calculated at the level of individual pairs of boundaries, one can
evaluate a segmentation algorithm by comparing each boundary it placed to the
closest boundary placed by each of the human annotators, and then taking a
micro-average of the comparison scores. To put these micro-averaged scores
into context, I established lower bounds and upper bounds on performance by
evaluating a “uniform” segmentation algorithm (lower bound) and treating the
original segmentations as if they had been algorithmically produced (upper
bound). The “uniform” segmentation algorithm produced segments having a
uniform length equal to the median length of the manually-created segments.
The number of observations is each group varied due to the fact that different
algorithms placed different number of boundaries, resulting in different
numbers of possible pairs for comparison.

Figure 2 shows the micro-average score across boundary judgment pairs for each
of the four groups. The number of observations is each group varied due to the
fact that different algorithms placed different number of boundaries,
resulting in different numbers of possible pairs for comparison. The scores
are not normally distributed, so a Kruskal-Wallis distribution-free test [13,
190–199] was performed to establish that the scores differed significantly
among all the groups ($H=329,3d.f.,\alpha=0.05$). Pairwise multiple
comparisons among groups using the Dwass, Steel, Critchlow-Flinger procedure
[13, 240–248] established that there were significant differences between each
pair except TextTiling and the uniform segmenter ($W^{*}=0.891,\alpha=0.05$).
In particular, the difference between BayesSeg and TextTiling was small but
significant ($W^{*}=3.83,\alpha=0.05$).

Figure 2: All differences are significant except between TextTiling and
Uniform.

Figure 3 compares the types of error made by the various segmenters.
TextTiling suffered compared to BayesSeg due to the number of extra boundaries
it placed (false positives). Setting a more conservative depth score threshold
for the TextTiling algorithm improved precision (fewer false positives) but at
the expense of recall (more false negatives), resulting in worse overall
performance.

Figure 3: TextTiling suffered compared to BayesSeg due to the number of extra
boundaries it placed.

Comparing the boundary edit distances between the automatic segmenters and
each individual annotator showed no significant differences between annotators
($H=4.67,\alpha=0.05$), indicating that the automatic segmenters did not agree
significantly more with any one annotator. A comparison of the boundary edit
distances between the automatic and manual segmentations of each interview
showed significant differences only between interview U-0012 and most of the
other interviews. Interview U-0012 had the second-highest level of agreement
among the human annotators and the highest ratio of manually placed boundaries
to potential boundaries, which may help account for the automatic segmenters’
better performance.

##  7 Discussion and Future Work

Segmentation and segment-level description could make oral histories more
accessible. But even though identifying topically coherent segments within
interviews is an accepted part of working with oral histories, people often do
not agree on the boundaries of those segments. In this study, annotators
agreed (exact match or “near miss”) on less than half of the boundaries they
placed. Despite this low agreement, there is some evidence that automatic
segmentation algorithms can produce segmentations that agree with human
judgment better than uniform segmentations do. In particular, the BayesSeg
algorithm’s richer language model and globally optimal boundary selection [4]
seem to improve its precision when segmenting oral history transcripts
characterized by varied diction and gradual topic shifts. Further progress
will depend both on clearer definition of the segmentation task, and on
evaluation of whether automatic segmentations are adequate for building
working systems for organizing oral history.

To improve agreement, the task of choosing topic boundaries in oral histories
should be further constrained. To address the issue of varying segmentation
granularity, annotators should be given a fixed number of boundaries to place
per transcript, so that they are choosing not how many boundaries to place but
only the locations of those boundaries. For example, the task of segmenting a
two hour interview transcript could be reformulated as choosing how to
distribute 11 segment boundaries (assuming a target granularity of ten minutes
per segment). The task could be further constrained by setting upper and lower
limits on segment size (for example a minimum of of five minutes and a maximum
of fifteen). Finally, rather than characterizing the task as one of choosing
extracts, which may lead to confounding “trimming” behavior, annotators should
simply be asked to place the boundaries at major topic shifts.

Ultimately, the value of automatic segmentations of oral histories will be
determined not by their quantitative agreement with manual segmentations but
by their usefulness in actual working systems. How “good” do automatic
segmenters need to be to help curators prepare oral histories for public
exhibition? What kinds of interfaces can be built to take advantage of
segmented content? How do different segmentation strategies help listeners and
readers find topics of interest in a long interview? Answering these questions
will require new publishing tools that incorporate segmentation into the
production workflow of oral history projects, and new finding aids that make
the segment, rather than the interview, the primary unit of description and
discovery.

##  8 Acknowledgments

This work was funded by the Institute of Museum and Library Services. Thanks
to the Southern Oral History Program for making the oral history interview
transcripts available, Greg Maslov for his TextTiling implementation, Jacob
Eisenstein for publishing the BayesSeg source code upon which my
implementation was based, and Kathy Brennan and Sara Mannheimer for their
annotation work.

## References

  * [1] Ashplant, T.G.: Anecdote as Narrative Resource in Working-Class Life Stories: Parody, Dramatization and Sequence. In: Chamberlain, M., Thompson, P. (eds.) Narrative and Genre, pp. 99–113. Routledge, London (1998)
  * [2] Beeferman, D., Berger, A., Lafferty, J.: Statistical Models for Text Segmentation. Machine Learning 34(1-3), 177–210 (1999), http://dx.doi.org/10.1023/A:1007506220214
  * [3] Choi, F.Y.Y.: Advances in domain independent linear text segmentation. In: Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference (NAACL 2000). Association for Computational Linguistics (2000), http://anthology.aclweb.org/A/A00/A00-2004.pdf
  * [4] Eisenstein, J., Barzilay, R.: Bayesian unsupervised topic segmentation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP ’08) (2008), http://dl.acm.org/citation.cfm?id=1613760
  * [5] Fleiss, J.L., Nee, J.C., Landis, J.R.: Large sample variance of kappa in the case of different sets of raters. Psychological Bulletin 86(5), 974–977 (1979), http://dx.doi.org/10.1037/0033-2909.86.5.974
  * [6] Fournier, C.: Evaluating Text Segmentation. Ph.D. thesis, University of Ottawa (2013), http://hdl.handle.net/10393/24064
  * [7] Fournier, C.: Evaluating Text Segmentation using Boundary Edit Distance. In: Proceedings of 51st Annual Meeting of the Association for Computational Linguistics. p. to appear. Association for Computational Linguistics, Stroudsburg, PA, USA (2013), http://anthology.aclweb.org/P/P13/P13-1167.pdf
  * [8] Franz, M., Ramabhadran, B., Ward, T., Picheny, M.: Automated Transcription and Topic Segmentation of Large Spoken Archives. In: Eurospeech 2003. pp. 953–956. Geneva (2003)
  * [9] Frisch, M.: Oral History and the Digital Revolution: Toward a Post-Documentary Sensibility. In: Perks, R., Thomson, A. (eds.) The Oral History Reader. Routledge, London, 2nd edn. (2006)
  * [10] Galley, M., McKeown, K., Fosler-Lussier, E., Jing, H.: Discourse segmentation of multi-party conversation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL ’03. vol. 1, pp. 562–569. Association for Computational Linguistics, Morristown, NJ, USA (Jul 2003), http://dx.doi.org/10.3115/1075096.1075167
  * [11] Hearst, M.A.: TextTiling: segmenting text into multi-paragraph subtopic passages. Computational Linguistics 23(1), 33–64 (Mar 1997), http://dl.acm.org/citation.cfm?id=972684.972687
  * [12] Hewins, G.H., Hewins, A.: The Dillen: Memories of a man of Stratford-Upon-Avon. Elm Tree Books, London (1981)
  * [13] Hollander, M., Wolfe, D.A.: Nonparametric Statistical Methods. Wiley, New York, 2nd edn. (1999)
  * [14] Lambert, D., Frisch, M.: Meaningful Access to Audio and Video Passages: A Two-Tiered Approach for Annotation, Navigation, and Cross-Referencing within and across Oral History Interviews. In: Boyd, D., Cohen, S., Rakerd, B., Rehberger, D. (eds.) Oral History in the Digital Age. Institute of Museum and Library Services, Washington, D.C. (2012), http://ohda.matrix.msu.edu/2012/06/meaningful-access-to-audio-and-video-passages-2/
  * [15] Lippincott, S.: An Exploratory Study of the Information Behavior of Users of Oral Histories. Master’s paper, University of North Carolina at Chapel Hill (2012), https://cdr.lib.unc.edu/record?id=uuid:4b19e483-bcef-462d-a8eb-947e496d95e5
  * [16] Malioutov, I., Barzilay, R.: Minimum cut model for spoken lecture segmentation. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL ’06. pp. 25–32. Association for Computational Linguistics, Morristown, NJ, USA (2006), http://dx.doi.org/10.3115/1220175.1220179
  * [17] Manning, C.D.: Rethinking Text Segmentation Models: An Information Extraction Case Study. Tech. rep., University of Sydney (1998), http://nlp.stanford.edu/cmanning/papers/SULTRY-980701.ps
  * [18] Stede, M.: Discourse Processing. Synthesis Lectures on Human Language Technologies 4(3), 1–165 (Nov 2011), http://dx.doi.org/10.2200/S00354ED1V01Y201111HLT015
  * [19] Thompson, P.: The Voice of the Past: Oral History. Oxford University Press, Oxford, 3rd edn. (2000)
  * [20] Toutanova, K., Klein, D., Manning, C.D.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1 (NAACL ’03),. pp. 252–259 (2003), http://dx.doi.org/10.3115/1073445.1073478
  * [21] Zhang, P., Soergel, D.: Knowledge-Based Approaches to the Segmentation of Oral History Interviews. Tech. rep., University of Maryland (2006)